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Eames Insights: How to carve out a successful career in data

  • Publish Date: Posted about 1 year ago
  • Author:by Jasper Ang

There are many key questions to consider when looking to make a career move or forward thinking around the success of your current position.

Just as the world is changing and data is becoming more important, so are the requirements and attitudes of employers and employees. We spoke to C-suite level leaders in the market to find out what they think is important for a career in data, by discussing three components; skills, qualities and attitudes.

What is important when looking at skillsets and transferable skills in the data industry?

  • If you are a data engineer, in data analytics or in data science your skillset is transferable across industry and/or sector, as you would know the backend and how to manage different data regardless of size and nature. Which is important in data. Vibhanshu Singh - Director of Analytics & Insights, The Coca Cola Company

  • There is surprisingly little that is not transferable in data. Everything looks quite universal for me, and I have worked in big and small; media, e-commerce, and finance industries. Still, you need coding skills (python + SQL), understanding of ML models and lifecycle, and know how data works. Data is penetrating every job. You don’t need to transfer to a data science team to be a data scientist. If you are in marketing, you can do marketing with data and ML. If you are in finance, you can do liquidity management with data and ML. This is relevant for every field and area. Just pick a challenge in your current scope and learn new skills as you go. Nikolay Novozhilov - Head of Data Science, Bank of Singapore

  • If the future trend is BYOS (bring your own stack) then we can expect statistics, problem solving, machine learning and data communication to be common transferable skills with tool skills such as using statistics, R/Python/STATA/SAS as unique skills. These tool skills will be driven by supply and demand, and looking into if companies adopt a strict follow one brand and no BYOS policy. The companies that don’t adapt to BYOS, will have greater difficulties finding talent unless they increase their compensation premium by up to 50%. Andrew Liew – Data Science Partner, Qicstart

  • Focus on the fundamental skills depending on the job role. These may include things like database queries (SQL), data visualisation (PowerBI and Tableau) and spreadsheets. For those who wish to do more advanced analytics or data science, being able to program in Python and manipulate large datasets using Pandas package and the use of data science libraries like scikit-learn, are highly transferrable, along with analytical and critical thinking and communication skills. Senior Data Scientist, Reinsurance sector

What qualities and attitudes can help you stand out in the data industry? 

  • It’s also important to have the skills to connect the main business outcome with generated data. Always keep an eye on the key technology trends and try to learn at least one to two new skills or tools in a year to stay relevant in the market. Vibhanshu Singh - Director of Analytics & Insights, The Coca Cola Company

  • Everything is changing fast. It’s important to keep up with the trends, but also keep testing everything yourself to tell apart the hype from real game changers. For example, the hottest thing now is chatGPT. Reading about it is not enough. You’ve got to try it out and see how it can fit into your work. Nikolay Novozhilov - Head of Data Science, Bank of Singapore

  • Focus on the fundamentals: statistics, business problem solving, data cleaning and communications, then pick a tool that you love to use and learn to be an expert. Find companies that follow a BYOS policy overtime so you can fit your skills (transferable and tool) comfortably to be successful. Andrew Liew – Data Science Partner, Qicstart

  • Create a visible portfolio, for example data science notebooks on Github and dashboards on Tableau’s public gallery. If you are interested in a particular industry, then try to understand what data is typically available for that industry, and what techniques are used to solve (data science) problems in that domain. There are many master's degree courses available, and this might be a good option for those wishing to gain more specialisation and seniority. MLOps (eg. version control, CI/CD) is gaining importance, and so is understanding of issues surrounding fairness and ethics with the use of AI. Courses related to these areas would be useful. Senior Data Scientist, Reinsurance sector

If you are interested in talking about market trends, or help with your career planning, please drop me an email at: