Can you introduce yourself and tell us a bit about your career to date?
Firstly, I’d like to express my gratitude for having the chance to share my journey in the field of Treasury with those looking to follow a similar path. My name is Dean Taylor, and I currently operate as the Global Head of Liquidity Stress Testing & Analytics within Group Treasury at HSBC.
My career journey began at UBS in the Treasury Change team, where I supported the implementation of regulatory liquidity metrics such as the LCR, NSFR, and Asset Encumbrance. This role marked my initial foray into the liquidity domain, offering comprehensive exposure to balance sheet management, product knowledge and stakeholder management. After 4 years at UBS, I moved to Credit Suisse to take on an opportunity working within Liquidity Measurement & Reporting in which I supported the daily production of regulatory metrics and regulatory submission processes each month. I had the opportunity to work closely with Liquidity Management during this time and this is where I started gaining an interest in working with the business. Not only did I want to produce the reports and distribute them to Liquidity Management, I wanted to learn how to take reported information and make informed decisions which enabled effective steering of the firm’s liquidity position. This aspiration led me to take on an opportunity to work within the Liquidity Steering team at Deutsche Bank. In this role, the objective is to ensure that the liquidity position of the firm is adequate at all times, working closely with the business and other stakeholders to ensure liquidity is managed effectively. This therefore requires high quality management information and forecasting capabilities to observe where the liquidity position is trending. The one common theme that underpinned each role was the extensive use of technology and data.
A defining moment in my career occurred while at UBS, following a dialogue with a colleague from the IT team. I had proposed what I thought was a simple business requirement, only to receive a delivery estimate of six months. At the time, with my limited understanding of technology processes, I assumed this timeline was standard. However, driven to verify this estimate and understand the complexities of my request, I embarked on learning basic programming and coding skills. This endeavour has not only resulted in technology becoming a passion of mine, but has also enhanced my ability to collaborate and communicate effectively with a diverse set of stakeholders. Furthermore, it has led me down a path of teaching programming to over 2,500 students online, covering languages and tools such as Python, SQL, Alteryx, and Tableau. Being part of a community of like-minded individuals who are passionate about technology is very fulfilling and I feel fortunate to be able to apply this in my daily role at work. I am deeply thankful to my former IT colleague for inadvertently setting me on this path. The six month build estimate sparked my curiosity in technology which has since become a key enabler in my career.
In 2022, I joined HSBC as Global Head of Stress Testing & Analytics and the team can be divided into two primary functions. On one side, the Liquidity Modelling team is tasked with overseeing the global liquidity models used in daily liquidity management. These models play a crucial role in evaluating our entities’ liquidity adequacy during times of stress. For instance, a model developer in the team would analyse retail deposit portfolios to predict customer behaviours in different stress scenarios, determining the quantum and speed of cash withdrawals that may be observed for different customer segments. The insights generated from these models are integral to our internal stress testing, enabling each entity to ascertain if they possess adequate liquid assets to meet expected outflows in times of stress. This role is both challenging and rewarding, offering a multitude of intriguing subjects for discussion with a wide range of stakeholders.
On the other side, the Data & Analytics team focuses on providing Liquidity Management with self-service analytical tools that facilitate the measurement and monitoring of daily liquidity positions. This empowers users to make well-informed decisions when steering the liquidity position of an entity. The skill set within the analytics team is more technologically advanced, developing essential capabilities that assist our treasury colleagues in efficiently managing liquidity risk. Developments include the automation of complex processing and controls as well as the design and build out of dynamic visualisation capabilities. This dual structure of our team ensures the development of best-in-class models, complemented by self-service management information (MI) and analytical tools, fostering transparency in an ever-evolving and dynamic field within Treasury. I feel fortunate to have held various engaging positions across several banks, each providing valuable learning opportunities and allowing me to expand my professional network. I would certainly recommend anyone who is looking to start a career in banking to consider a role within the Treasury space. It is a very rewarding role with exposure to so many parts of the bank and so many different stakeholder groups. If I had my time again and could choose any path to take, I would seek a similar route.
Coding is a broad term, can you breakdown what is meant by this catch all phrase? (Backend Development/Model Outflows/ Creating Visuals)
Coding itself is quite a specific skill. It is what coding enables that is very broad, but that is what makes it an exciting skill to learn! Coding is a general term that refers to the process of writing instructions for a computer to execute. The instructions are written using programming languages and it is these instructions that enable us to create software, applications, websites and so much more. The terms “coding” and “programming” are often used interchangeably, though there are nuances in their meanings. Generally, both terms describe the process of crafting instructions for computers. However, as a rule for anyone new to this space, “coding” is typically used in more informal contexts and refers to writing code for smaller, more specific tasks. On the other hand, “programming” is used in more formal settings and encompasses the entire software development lifecycle, indicating a comprehensive and strategic approach to software creation.
With a coding skill set, the opportunities are virtually limitless. It allows for exploration across various avenues, depending on your interests and the problems you aim to solve. In the context of a Treasury function, coding is instrumental in creating visualisation tools, conducting data analytics, leveraging machine learning and AI for modelling and analysis purposes, and automating routine tasks. The choice of programming language often depends on the task at hand and the developer’s preference, with certain languages better suited for specific applications, but for those who have a specific area of interest, the following languages are a great place to start.
Creating Visualisation Tools
Although less code is required for the following tools, Tableau, QlikSense, and Power BI are highly regarded for creating data visualisations due to their user-friendly interfaces, powerful data handling capabilities, and extensive range of advanced visualisation options. They enable efficient processing of large datasets and offer interactive dashboards for in-depth data exploration. These tools support real-time data analysis, essential for timely decision-making, and facilitate collaboration and sharing among users. Additionally, they are scalable, customisable, and accessible on mobile devices, making them suitable for a wide range of business needs. Their strong user communities and support resources further enhance their usability and effectiveness in deriving actionable insights from data. With regards to programming languages, Python, R, and JavaScript are excellent for creating data visualisations due to their distinct strengths. Python is user-friendly and versatile, offering a rich set of libraries like Matplotlib and Seaborn for diverse visualisations. Its straightforward syntax and strong integration capabilities make it widely accessible and practical for various applications. R excels in statistical analysis, making it ideal for complex data visualisations. It has specialised packages like ggplot2 for creating high-quality graphs and shines in handling and manipulating large datasets, backed by a robust academic and research community. Finally, JavaScript is key for dynamic and interactive web-based visualisations, leveraging libraries like D3.js for a wide range of visual styles. Its ability to create real-time, interactive graphics and its universal browser compatibility make it indispensable for web applications.
Together, these languages offer accessibility, statistical depth, and interactive capabilities, making them top choices for effective and engaging data visualisation tools. If I had to pick one to start with, my recommendation has always been to pursue Python as a language to learn due to ease of use and the extensive library options available.
Performing Data Analytics
Certain programming languages stand out for their effectiveness in conducting data analysis, which is clearly an important aspect of any treasury team. Python is a top choice due to its versatility and comprehensive library support, including Pandas for data manipulation and NumPy for numerical analysis, making it ideal for a range of financial data analysis tasks. Its user-friendly nature also makes it accessible for both beginners and advanced users in treasury departments. R, known for its specialisation in statistical analysis, is crucial for tasks such as risk assessment and forecasting, offering robust packages like dplyr for data manipulation and ggplot2 for sophisticated data visualisation. SQL is indispensable for managing and querying financial data stored in relational databases, a frequent necessity in treasury operations. Additionally, Excel VBA plays a significant role in treasury by automating repetitive tasks and facilitating complex financial calculations within Excel, a staple in financial functions. Together, these languages provide a comprehensive toolkit for financial modelling, risk assessment, liquidity analysis, and reporting, crucial for effective treasury management. SQL was my first taste of coding and has been used extensively in my career in order to automate many time consuming tasks, so I would certainly recommend learning the basics of SQL to any aspiring data analyst.
Using Machine Learning and AI
There are several programming languages that stand out for their suitability in machine learning (ML) and Artificial Intelligence (AI). In my opinion, Python is the frontrunner, renowned for its ease of use and powerful libraries like Scikit-learn, TensorFlow, and PyTorch, making it ideal for developing predictive models and AI algorithms for tasks such as cash flow forecasting. R, with its strong statistical and analytical capabilities, is another excellent choice for data-intensive ML tasks, offering a range of packages for statistical analysis and machine learning. While SQL is not used for ML directly, its strength in data management is crucial in the data preparation stage of ML workflows. For integrating ML and AI into existing enterprise financial systems, Java’s robustness and scalability make it a preferred choice. Scala, often used with Apache Spark, is also suitable for real-time analytics and handling large-scale financial data. Collectively, these languages provide a comprehensive toolkit for implementing ML and AI effectively within treasury operations.
Automating Repetitive Tasks
In treasury functions, automating repetitive tasks is key for operational efficiency, and several programming languages are well-suited for this purpose. Python is a top choice due to its simplicity and powerful data handling libraries, making it ideal for routine tasks like data entry, report generation, and file management. VBA, integrated with Microsoft Office, excels in automating tasks in Excel, a staple in financial functions. For database management and data manipulation, SQL is indispensable, streamlining data extraction and loading processes. R also finds use in automating data processing tasks, especially those requiring statistical analysis. In specific system environments, Bash scripting is useful for automating file and system management tasks in Linux or Unix systems, while PowerShell serves a similar purpose in Windows environments. These languages collectively offer a comprehensive toolkit for automating a wide range of tasks in treasury operations, enhancing accuracy and efficiency.
RPA tools, aka Robotic Process Automation Tools, like UiPath, Automation Anywhere, and Blue Prism provide visual interfaces to automate repetitive tasks without extensive coding knowledge. Alteryx is also a fantastic option for those who have the need to connect disparate data sources together, transform and present data for onward consumption, although there is a cost to using low-code / no-code tooling. What I tend to find is that people who pick up platforms like Alteryx start to improve their data literacy, making for an easier time when learning to code. So, obtain a license if you have the opportunity!
Integration of Coding for Comprehensive Solutions
Although not necessarily relevant for a treasury risk SME, full-stack developers often use a combination of languages (e.g. JavaScript for front-end, Python or Java for back-end) to create end-to-end solutions that involve visualisation, data analytics, machine learning, and automation. So, if you want to get involved with all aspects of a programme, learning multiple languages will certainly be beneficial. These skills do not need to solely sit inside the walls of an IT department.
In short, coding languages provide a wide range of tools and libraries that empower developers and data scientists within a Treasury function to create sophisticated solutions for visualisation, data analytics, machine learning, AI, and task automation. The choice of language depends on the specific requirements of the task at hand and the preferences of the developers involved. A coding skill empowers an individual to create, innovate, and solve problems across a wide range of fields, making it an essential skill in today’s technology-driven world. There has never been a better time to learn coding skills for use within the Treasury domain.
We’re eager to understand the role data analytics and coding plays in today’s treasury function? Can you provide an insight into where and how these skills can be applied within treasury risk management? Can you provide a practical example or project that you’ve worked on where these skills have been best deployed?
Historically, the core skill set required for a Treasury SME is knowledge of regulation, products, businesses and a strong understanding of the balance sheet. However, there are many problem statements at hand that require a technology solution. Datasets are getting bigger and bigger and the need to consume data in an efficient and timely manner is as important as ever. The traditional software delivery lifecycle starts with a written business requirement to an IT function to deliver the intended solution, and although there is nothing wrong with this approach for core deliverables, there can often be longer lead times to obtain the required solution. For some requirements, the solution may simply be at your fingertips if you possess a coding skill set and do not necessarily have to follow the typical delivery lifecycle. Observing Treasury teams in 2024, it is clear to see that data analytics and coding have become integral to treasury risk management, enabling more precise and efficient handling of financial data, risk assessment, and decision-making processes. These skills allow treasury departments to analyse large volumes of financial data, forecast cash flow, assess risks, and optimise investment strategies.
One of the primary applications is in liquidity risk management. By using coding and analytics, treasuries can develop models to predict future cash flows and liquidity requirements under various scenarios. This involves analysing historical data, market trends, and other relevant financial indicators to forecast potential risks and opportunities.
Another crucial area is in the management of foreign exchange and interest rate risks. Here, coding and data analytics are used to create models that simulate different market conditions and their impact on the organisation’s exposure. These models help in formulating hedging strategies to mitigate risks associated with currency and interest rate fluctuations.
In terms of a practical example, I have been fortunate to work on internal stress testing capabilities at Credit Suisse, Deutsche Bank and HSBC. One common problem is with respect to visibility of data. A project in which coding has added tremendous value in Treasury is with respect to the roll-out of internal stress testing and forecasting capabilities, including self-service analytical capabilities, delivered in partnership with IT. In this example, the infrastructure, backend logic and database security was managed by IT. However, all visualisations, MI and analytical capabilities were developed and supported by business personnel with technical skill sets. This partnership led to one of the most widely used tools in the bank with over 250,000 views within 3 years. This is not because the visualisations were any better than any other report. It was because the operating model allowed us to keep the front end relevant at all times by leveraging technical skill sets coupled with a strong understanding of the business, leading to the expansion of capabilities as and when required..
To summarise, data analytics and coding have revolutionised treasury functions, offering sophisticated tools for risk management, decision-making, and process automation. These skills enable treasuries to be more proactive and strategic in managing financial risks and optimising financial performance.
Liquidity management happens in real time, how can the ability to code benefit a risk manager in this space?
Analytical capabilities and visualisations allow an end user to address an array of questions. The best capabilities enable all questions to be answered in a self service fashion without having to leave an application. Unfortunately, it is very difficult to write business requirements that answer every question. So sometimes you need to dive into data and seek the answer by utilising a coding skill set. Having coding experience would allow you to perform these tasks.
As an example, imagine you were given the following task. “Can you please take the last 6 months of data, consisting of 100 million records, and tell me which is the most volatile book code”. As a risk manager, what are the options available to you? If you only have excel as a tool to perform data analysis, you will fall at the first hurdle in being able to perform analysis on the dataset due to the row restriction excel has within it. And you’ll tend to find that the bigger datasets are usually where hidden insights are discovered. With this example, the ability to code can provide significant advantages to a risk manager involved in real-time liquidity management. There are plenty of examples I could provide, but a few that come to mind are:
Risk Models and Scenario Analysis
Developing and implementing risk models is a critical aspect of liquidity management. Coding allows risk managers to create sophisticated models that consider various market scenarios and factors influencing liquidity. This facilitates real-time scenario analysis, helping them assess the potential impact on liquidity positions.
API Integration and Connectivity
Many financial institutions use different systems and platforms for trading, risk management, and data analysis. Coding skills enable risk managers to integrate these systems seamlessly through APIs (Application Programming Interfaces). This integration ensures a real-time flow of data between different tools and platforms, providing a comprehensive view of liquidity positions.
Customised Reporting and Dashboards
Risk managers can create customised reporting tools and dashboards tailored to their specific needs. Coding skills enable the development of interactive and dynamic dashboards that display real-time liquidity metrics, helping risk managers monitor key indicators and make informed decisions.
Machine Learning for Predictive Analytics
Machine learning algorithms can be applied to predict liquidity needs and identify potential risks in real time. Coding skills are essential for developing, implementing, and maintaining machine learning models that analyse historical and real-time data to provide predictive insights.
Stress Testing and Simulations
Writing code is essential for creating and running stress tests and simulations in real time. Risk managers can use coding to model various stress scenarios, assess the impact on liquidity positions, and develop contingency plans to address potential challenges.
In short, coding skills empower risk managers in real-time liquidity management by enabling efficient data processing, algorithmic trading, advanced risk modelling, system integration, and the development of customised tools. These capabilities contribute to more effective decision-making and risk mitigation in dynamic financial environments.
Python/R/SQL appear to be the most in demand coding languages based on the job descriptions we receive at Beacon Search. For those without experience of this space what does being able to code allow a risk manager to do?
(Reporting/Dashboard Creation/ Liquidity Steering/ MI/ Stress Test Scenario Planning)
As you mention, Python, R, and SQL are widely used coding languages and are often used in the finance domain, each serving specific purposes. There are endless use-cases for such languages, some of which have been covered earlier in this interview, but here are a few specific examples of what each language can produce and some of the libraries that are often used when designing a solution:
Python
Data Analysis and Visualisation
You can use Python libraries such as Pandas and Matplotlib/Seaborn to analyse financial data sets, perform stress testing calculations, perform statistical analysis and create visualisations (e.g. time series plots, candlestick charts,).
Algorithm Trading
Developing trading algorithms and strategies using Python’s quantitative finance libraries like Quantlib or implementing algorithmic trading strategies with platforms such as the Python-based back testing library Backtrader.
Risk Management
Building risk models and simulations to assess portfolio risk using Python. Libraries like SciPy and NumPy can be used for statistical analysis, and scikit-learn for machine learning-based risk modelling.
R
Statistical Analysis
Conducting advanced statistical analysis on financial data using R’s statistical packages, such as conducting regression analysis, hypothesis testing, or time-series analysis.
Data Visualisation
Creating sophisticated and customisable visualisations for financial reports or presentations using R’s ggplot2 package.
Risk Modelling
Developing and implementing financial risk models, stress testing, or value-at-risk (VaR) calculations using R’s quantitative finance packages like quantmod or RiskMetrics.
Time Series Analysis
Analysing and modelling time series data for forecasting financial trends or predicting market movements using R’s time series analysis packages.
Shiny Apps for Interactive Dashboards
Building interactive and dynamic financial dashboards or reporting tools using R’s Shiny framework.
SQL (Structured Query Language)
Database Management
Managing and querying financial data stored in relational databases (e.g., MySQL, PostgreSQL, SQL Server) using SQL for tasks such as retrieving specific records, aggregating data, or joining tables.
Data Cleaning and Transformation
Cleaning and transforming raw financial data in a database, such as standardising formats, handling missing values, or creating derived variables using SQL queries.
Reporting and Analysis
Extracting data from a database to create reports and conduct ad-hoc analysis. SQL is often used for data extraction tasks that support financial reporting.
Data Integration
Integrating data from multiple sources by using SQL to join or merge datasets, ensuring a comprehensive view of financial information.
Data Security and Access Control
Implementing data security measures and access controls by using SQL to manage user permissions and restrict access to sensitive financial data. In practice, these languages are often used in combination within the finance function to leverage their respective strengths in data analysis, modelling, visualisation, and database management. The choice of language depends on the specific requirements of the task at hand and the preferences of the finance professionals involved.
How big a role has data analysis become over the past few years and how can data visualisation/dashboards allow risk managers to better perform analysis and communicate results/trends to stakeholders?
Over the past few years, the role of data analysis has grown significantly across various industries, and Treasury is no exception. The increasing volume and complexity of data, coupled with advancements in technology, have made data analysis a crucial component in the decision-making process. In the context of treasury risk management, data analysis plays a vital role in assessing, mitigating, and communicating risks. With the proliferation of data sources, the advent of big data technologies, and advancements in analytics tools, organisations across various industries are recognising the value of data-driven decision making. This is particularly true in the finance sector, where data analysis plays a significant role in risk management, strategic planning, and overall business intelligence. It involves using data to make informed decisions, identify and mitigate risks, allocate resources efficiently, and ensure regulatory compliance. Data analysis helps risk managers understand patterns and trends in data, anticipate future scenarios through predictive modelling, and maintain adherence to regulatory standards. Complementing this, data visualisation and dashboards are crucial tools. They simplify the communication of complex data to stakeholders, facilitate real-time monitoring of risks, and provide a comprehensive view of various risk factors. These visual tools aid in scenario analysis and can be customised for different audiences, enhancing understanding and decision-making. Interactive dashboards enable users to explore data more deeply, making it easier to identify trends, outliers, and anomalies. Overall, data analysis and visualisation play a pivotal role in guiding risk managers to make strategic, data-driven decisions.
In summary, data analysis has evolved into a critical component of business strategy, and the use of data visualisation and dashboards enhances the ability of risk managers to analyse and communicate insights effectively. These tools contribute to a more transparent, informed, and agile approach to risk management in dynamic business environments such as Treasury.
How do you expect the role of coding to contribute and evolve within treasury over the coming years?
In the coming years, I expect individuals with a coding skill set to play a transformative role in treasury functions in several key areas. It will be instrumental in automating routine tasks like data entry, reconciliation, and report generation, thereby improving accuracy and freeing up professionals to focus on strategic activities. The use of coding for advanced data analytics will grow, with languages like Python and R being used to analyse large datasets, identify patterns, and extract insights crucial for risk management and investment decisions. Machine learning and AI will become integral, with coding skills being essential for predictive analytics and optimising decision-making processes. The integration of treasury systems through APIs will rely heavily on coding skills for seamless data flow and communication between various platforms and services. Coding will continue to be vital in enhancing risk modelling, using advanced algorithms and models for comprehensive risk assessments. Real-time decision-making will be supported by coding, enabling the development of dynamic dashboards and visualisations based on real-time data. Regulatory compliance automation will become increasingly important, with coding playing a key role in ensuring adherence to evolving regulations.
Furthermore, treasury management systems and platforms will evolve to offer more customisation options, where coding skills will be valuable in tailoring these systems to specific organisational needs. Finally, collaboration with fintech companies will likely increase, with coding skills being essential for integrating and customising fintech solutions to enhance treasury operations.
In short, I would expect the role of coding in treasury to expand as technology continues to advance. Treasury professionals with coding skills will be better positioned to leverage emerging technologies, automate processes, and contribute to more efficient and strategic financial management within organisations.
For the first time an American treasurer has instructed us that they will only hire candidates with coding experience in the future, do you expect this to become the norm?
Although coding experience opens up opportunities in Treasury, there is certainly the need to create a diverse range of skill sets in Treasury to create a high performing function. So while it’s challenging to predict with absolute certainty, the trend of treasurers and finance professionals seeking coding experience is likely to continue growing. But treasurers would need to consider a variety of factors when assessing whether a coding skill set is a necessity for the role in question. Although there are many factors that influence whether a coding skill set is required for a role within treasury, I believe the below points are key drivers:
Increasing Emphasis on Technology
As technology continues to play a central role in finance and treasury functions, the demand for professionals with coding skills is likely to rise. Companies are recognising the value of employees who can navigate and leverage technology to enhance efficiency, automate tasks, and make data-driven decisions.
Integration of Data Science and Analytics
The integration of data science and analytics in treasury operations is becoming more common. Coding skills are essential for professionals to work with large datasets, develop algorithms, and extract actionable insights. The ability to code enables treasurers to effectively harness the power of data analytics for strategic decision-making.
Automation of Routine Tasks
Coding proficiency is crucial for automating routine and repetitive tasks within treasury. With an increasing focus on efficiency and reducing manual effort, treasurers may prioritise candidates who can contribute to process automation through coding.
Fintech Innovation
The financial industry is witnessing rapid innovation driven by fintech. Treasurers who understand and can work with fintech solutions often require coding skills for effective integration and customisation. Fintech partnerships and adoption may further encourage a preference for candidates with coding experience.
Market Demand and Competition
If the demand for candidates with coding experience remains high and is seen as a competitive advantage, more treasurers may adopt this hiring criterion. The desire to stay competitive in the talent market may prompt other organisations to follow suit.
Specialised Technology Requirements
Treasury roles, especially those involving risk management, financial modelling, and systems integration, may require specialised technology skills. Coding can be essential for professionals to meet specific technology requirements associated with these roles.
Educational Emphasis on Coding
As educational institutions increasingly emphasise coding skills in finance and business programs, the pool of candidates with coding experience may naturally grow. This could contribute to a shift in hiring preferences among treasurers. It’s important to note that while coding skills are valuable, they may not be the sole determinant of a candidate’s suitability for a treasury role. Soft skills, domain knowledge, and the ability to collaborate effectively are also crucial. Additionally, the specific coding languages and skills required may vary based on the organisation’s technology stack and the nature of the treasury functions. In conclusion, while it’s plausible that the preference for candidates with coding experience may become more common, the trend is likely to depend on industry dynamics, technological advancements, and the evolving expectations of treasurers and financial leaders.
What courses can you recommend to those candidates who are looking to begin or deepen their experience with coding? Where should they start?
First of all, coding isn’t for everyone, and that’s OK. In fact, it can be one of the most frustrating journeys to pursue. A common pattern of study for many students is the following. Someone enrols on a Python course with every intention of learning how to code. They put in hours of study, and it feels like it’s getting easier. Then you go to work ready to apply your newfound skills, but you have no use cases to apply it to. Then life takes over and a few months later you want to learn to code again. You then go back to a Python course with every intention of learning how to code… and the story repeats. Practical application of a programming language with different problems to solve is the most important thing to do to enhance your understanding and skill set. For those embarking or advancing on a coding journey, a multitude of educational paths are available, catering to various levels of expertise and interests. Absolute beginners can build a solid foundation in computer science principles and coding basics through introductory courses like Harvard’s CS50 on edX or MIT’s Introduction to Computer Science and Programming Using Python. Interactive platforms such as Codecademy and Khan Academy offer a diverse range of programming languages, including Python, JavaScript, and HTML/CSS, ideal for hands-on learning. For foundational programming skills, courses like Coursera’s Python for Everybody, Udemy’s Complete Python Bootcamp, or JavaScript and web development courses on platforms like Udemy and LinkedIn Learning are beneficial.
Those interested in data science and analytics can turn to DataCamp or Coursera’s Data Science Specialisation for Python and R programming, while SQL enthusiasts can learn through Khan Academy and SQLZoo.
For more specialised fields like machine learning and AI, Coursera’s Machine Learning course by Andrew Ng and its Deep Learning Specialisation provide in-depth knowledge. Web development learners have resources like The Odin Project and FreeCodeCamp, offering full-stack curriculums. Software development aspirants can pursue edX’s Software Development MicroMasters or Udacity’s Nanodegree programs in areas like front-end or full-stack development. Advanced learners looking to specialise further can find specialised courses on platforms like Udemy, Coursera, and Pluralsight, covering technologies like React and Node.js.
To start, it’s crucial to identify your area of interest, whether it be web development, data science, or another field. Choosing the right language, such as Python for versatility, JavaScript for web development, or R for data analysis, is key. Setting clear learning goals based on career aspirations or personal development helps in selecting appropriate courses.
By starting with foundational courses and progressively moving to more specialised ones, you can systematically deepen your coding expertise. It’s important to remember that the journey of learning to code is iterative and requires patience and continuous exploration. It’s also important to combine online courses with hands-on coding projects to reinforce your learning. The one thing I have benefitted from personally is taking advantage of coding communities and forums. If there are specific domains that interest you, get involved and try contributing to open-source projects as this can provide valuable real-world experience that you can take into any treasury role.
How difficult is it to hire candidates in this space? Do those with quantitative skill sets demand higher salaries?
Attracting candidates with technical skills, particularly in coding and data science, is becoming increasingly challenging in today’s job market and is expected to become even more so in the future. This difficulty arises from several factors, including the rapid pace of technological advancement, the growing demand for tech skills across various industries, and the evolving landscape of education in technical fields.
As technology continues to permeate every sector, the demand for skilled technical professionals is soaring. This demand is not just limited to tech companies but extends to finance, healthcare, retail, and many other industries seeking to leverage technology for innovation and efficiency. This widespread demand creates a highly competitive job market, where top tech talent often has multiple attractive offers to choose from, making it harder for individual employers to stand out and attract these candidates.
Moreover, the technological landscape is constantly evolving, with new programming languages, tools, and methodologies emerging regularly. Staying abreast of these changes and finding candidates who are not only skilled in current technologies but also adaptable to future developments is a significant challenge. Employers must seek candidates who demonstrate continuous learning and adaptability, traits that are essential in a field where change is the only constant.
Adding to this complexity is the changing nature of education in technical fields. Coding and technical skills are increasingly being taught at schools and universities, making them more common and accessible. While this broadens the overall pool of tech-savvy candidates, it also means that basic coding skills alone are no longer sufficient to differentiate candidates in the job market. Employers now look for candidates with advanced, specialised skills or unique combinations of technical expertise and industry-specific knowledge. This shift elevates the bar for what is considered a desirable skill set, making the recruitment process even more challenging.
Furthermore, as more educational institutions incorporate tech education into their curricula, the expectations and aspirations of new graduates are also evolving. Young professionals entering the job market are often seeking roles that offer not just competitive salaries, but also opportunities for continued learning, career growth, and work on cutting-edge projects. Employers, therefore, need to not only offer attractive compensation packages but also invest in professional development, innovative projects, and a dynamic work culture to appeal to this new generation of tech talent.
Candidates with strong coding and quantitative skill sets tend to command higher salaries, a reflection of the high value and demand for their expertise in the market. Several factors contribute to these elevated salary expectations. Firstly, the level of experience plays a significant role; candidates with a substantial track record and proven success in their field typically seek higher compensation. Furthermore, candidates with advanced degrees in quantitative fields, such as finance, statistics, or computer science, often have a higher salary threshold due to their specialised knowledge and skills.
Geographic location is another critical factor influencing salary expectations. In major tech hubs or financial centres like Silicon Valley, New York, or London, where the cost of living is high, salaries for technical roles are correspondingly higher to attract and retain talent. Conversely, in regions with a lower cost of living, salary expectations might be more moderate, although this can vary depending on the local demand for such skills.
The specific industry and the size of the company also play a role in determining salaries. For instance, fintech startups, which are often at the forefront of technological innovation, may offer competitive salaries and additional incentives like stock options or bonuses to attract the best talent. On the other hand, larger, more established financial institutions might offer higher base salaries, along with comprehensive benefits and job stability. In today’s job market, candidates often value a comprehensive compensation package that includes health benefits, retirement plans, opportunities for professional development, and work-life balance options like flexible hours or remote working possibilities.
To summarise, while the proliferation of technical education is creating a larger pool of candidates with basic tech skills, the challenge for employers lies in attracting those with advanced, specialised capabilities. The task of hiring candidates with coding and technical skills is multifaceted and demanding, with a competitive market driving high salary expectations. Employers need to navigate various factors, including experience, education, geographic location, industry trends, and overall compensation packages, to attract and retain the skilled professionals essential for their business needs.
The competitive job market, the rapid pace of technological change, and the evolving expectations of tech professionals make it increasingly difficult for employers to attract and retain the top technical talent they need. Employers must navigate these complexities by offering compelling compensation, opportunities for advancement, and an engaging work environment that aligns with the aspirations of modern tech professionals.
If anyone would like to discuss any of these topics in more detail I am contactable via LinkedIn. Also, if you are interested in building up your technical skill set to support your risk management skill set, then I can also provide you with free online courses which you can complete in your own time. They serve as a nice introduction to the world of coding and data analytics, so please reach out to me to obtain a link that will enable you to enrol for free.
It’s been nice sharing my views with you all today. Best of luck in your future endeavours!
