DATA SCIENCE

Including resources for how to build those skills

If you have been a data scientist for a while, sooner or later you’ll notice that your day-to-day has shifted from a VSCode-loving, research paper-reading, git-version-committing data scientist to a collaboration-driving, project-scoping, stakeholder-managing, and strategy-setting individual.

This shift will be gradual and almost unnoticeable but one that will require you to put on different hats to ensure data initiatives are on track and impactful. It is at this point that you will start to notice the need for honing some business skills, in addition to your usual data science skills. This will also be a good indication that you are ready to aim for senior tech leadership roles such as Principal, Lead, or Staff DS.

Here are my top three picks that have been quite useful as I took on a data science leadership role in an FTSE 100 company, but ones that would be equally useful in a scrappy startup environment.

1. Get fluent in finance

Knowing how a business makes money is crucial regardless of the size of the company and your role in it. Unfortunately, a lot of data science work often happens in silos where the problem statement or hypothesis, or analysis workflow is top-down and may lack direct alignment with the company’s financial goals.

As you take on a more senior leadership role with the team, it is essential that you speak the language of business. Having a broad understanding of terms like CapEx vs. OpEx, EBITDA margin, amortization, blended CAC, churn cohorts, fair share index, etc. is helpful when you are communicating the results to the higher-ups. This way, you can tailor your insights to highlight how data science-driven initiatives will impact these areas, making your analysis more relevant and convincing to financial stakeholders.

Did you know Apple spent $110 billion on stock buybacks in 2024. Why? Fewer shares in the market = higher earnings per share (EPS), which boosts the stock price.

Knowing your numbers can benefit both you and the company: Understanding your numbers means that you know what’s working and not working for the business, identify areas for growth, and make sound financial choices based on data. For instance, instead of just showing improved model accuracy, one could demonstrate exactly how the predictions would impact the bottom line.

Similarly, by showcasing how your work directly contributes to the company’s financial success, you can even negotiate better pay for yourself!

But it goes beyond just communication. This knowledge opens doors to opportunities many data scientists miss. For instance, there are schemes that allow you to apply for tax rebates on your company’s CapEx that are associated with R&D activity (like patent-related costs, specialized software licenses, etc).

I have seen teams who were able to secure funding by understanding these financial mechanisms and positioning their ML infrastructure investments as R&D initiatives.

Likewise, there are certain government grants you or your company may be eligible for, depending on the space you are in. For instance, USDA (United States Department of Agriculture) offers grants and funding for projects in agri-tech innovation.

How to build this skill?

• Read books on finance to quickly grasp key terms and learn from case studies of other companies in the same niche as you (worst case scenario — you either fail fast or best case scenario — you learn about common pitfalls to avoid).
  If you don’t have the time to read books end-to-end, at the very least, get familiarized with their key ideas. I use AcceleratEd to get book summaries but there are other options that you choose from that I have discussed in this article.
  P.S. Here is my book collection for upskilling in finance, including books like The Alchemy of Finance, Value Investing, and One Up On Wall Street.
• Consume content from YT channels like TheFinanceStoryteller and Investopedia who break down complex finance topics into bitesized chunks.
• Keep an eye out for bursaries and grants applicable to your business.
• Shadow your COOs, Operations Manager, or, in some cases, even your POs (mine has been god-sent in helping me understand value calculations in the healthcare sector and improving my corporate finance understanding).

2. Keep Up With The Curve

Love it or hate it, but you can’t deny the fact that the AI/ML/Generative AI field is moving at an unprecedented rate. I have often read news articles describing technology X replaced technology Y and I am left thinking — what is technology Y!

On average, about 8000 new research papers (in Computer Science category) are published on arXiv every month! [Source]

To provide any sort of thought leadership in this new role, your industry, and technological awareness need to operate at two levels — local and global.

Keeping up with the local curve involves staying updated with the latest tools, techniques, and trends. In practical terms, this would translate as knowing (a) which models sit on top of the leaderboard for your usecase (be it forecasting, generative AI, or computer vision), (b) any new groundbreaking frameworks that would be game-changers for your field (for instance, Baidu recently unveiled iRAG technology that addresses the issue of hallucinations in image generation), and (c) advancements in DevOps/LLMOps/MLOps that could streamline workflows and improve efficiency.

Keeping up with the global curve means acknowledging the bigger picture around the tech field— understanding how innovations are shaping industries and the broader ethical and societal impacts of these technologies — especially as governments around the world are taking steps to regulate the tech domain.

In practical terms, this could mean keeping up-to-date with regulations in the field in which you operate (legal, healthcare, FMCG, etc) and checking compliance with relevant guidelines.

For instance, the European Union’s AI Act 2024, which came into effect recently, has detailed guidelines on the dos and don’ts surrounding the development, deployment, and use of AI, including guidelines such as mandatory watermark to content generated by AI.

Similarly, keeping track of the big tech players like NVIDIA, OpenAI, Anthropic, etc. is even more important to anticipate short and long-term technological shifts for your business. A short-term example would be the recent news of the OpenAI-Microsoft partnership turning sour, which could impact any ongoing projects if you rely on Microsoft’s Azure OpenAI as your LLM provider.

A long-term example is the recent investment in nuclear power projects by companies like Microsoft, Amazon, and Google, to meet the growing demand for high energy consumption by large language models (LLMs), often seen as a bottleneck for AI advancements. A stable, predictable, and carbon-free energy source could mean long-term cost savings for your AI-driven business.

How to build this skill?

• Get a daily dose of tech news via apps (like Curio) or websites like HackerNews.
• Subscribe to a couple of weekly AI newsletters, or as many as you can realistically keep up with given your workload. I am highly self-aware and my only go-to is The Batch.

3. Be a communication ninja

For a lucky few who step up from data scientist into this new leadership role, soft communication skills — useful for managing teams, data storytelling, and cross-team collaboration — come naturally to them. For the rest, there’s hope! With practice, achieving any skill is possible.

And, before you ask why this is crucial — Imagine not knowing how to pitch your excellent data product to a group of non-technical VCs and investors. Or, an effective way to communicate insights from your week-long EDA process. Or, the right way to motivate your brilliant but overwhelmed data scientists during a critical product launch.

Stepping into a leadership position means being firm but polite, clearly explaining what the team needs to do, and being crystal clear with stakeholders on technical limitations between their ask and what’s within the realm of possibilities — keeping in mind constraints like cost, latency, etc.

It means staying cool when a stakeholder says ‘ChatGPT can do this in seconds’ or when someone demands ‘a 100% accurate model.’

To deliver this effectively, you need to learn the different dynamics at play. You need to be more diplomatic and rational rather than reacting impulsively when someone suggests ‘trying these 20 ideas that came up during the meeting’ or using inappropriate verbal and non-verbal cues when you can clearly detect scope creep.

How to build this skill?

• Again, books can be your best friend here. Here is my book collection for managing team dynamics, including books like Emotional Intelligence 2.0, The Five Dysfunctions of a Team, and Crucial Conversations: Tools for Talking When Stakes Are High, Made to Stick. I recently wrote about how these books have been insanely useful for saving my sanity as a tech lead.
• (Books can only take you so far so step up to) Lead stakeholder-facing meetings at work. Nothing beats a hands-on experience.
• Volunteer for roundtable discussions and fireside chats at conferences and seminars. These formats are more relaxed and take the pressure off compared to when you’re the only one presenting and others are passively listening. Back up your discussion points with facts and shreds of evidence from books, recent news, and reputed research papers to ensure your argument holds weight.

Final Thoughts

It might be scary to transition from a core data scientist role to a thought leader who is looked up to define, foresee, and shape the strategic direction of the data team.

But you need to remember that, as a person with both tech and business skills in your arsenal, no one is better positioned than you to take the reins.

You understand the technology behind the black box models, which makes finding feasible tech solutions easier, and you also have the business acumen to best align these solutions with business objectives.

I hope this article was useful and should an opportunity present itself, you’ll grab it with both hands. Watching your company/team/BU (business unit) succeed and knowing you’ve played a part in their growth can be a truly rewarding experience.

---

3 Business Skills You Need to Progress Your Data Science Career in 2025 was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.

​DATA SCIENCEIncluding resources for how to build those skillsImage by Author. Created using MidjourneyIf you have been a data scientist for a while, sooner or later you’ll notice that your day-to-day has shifted from a VSCode-loving, research paper-reading, git-version-committing data scientist to a collaboration-driving, project-scoping, stakeholder-managing, and strategy-setting individual.This shift will be gradual and almost unnoticeable but one that will require you to put on different hats to ensure data initiatives are on track and impactful. It is at this point that you will start to notice the need for honing some business skills, in addition to your usual data science skills. This will also be a good indication that you are ready to aim for senior tech leadership roles such as Principal, Lead, or Staff DS.Here are my top three picks that have been quite useful as I took on a data science leadership role in an FTSE 100 company, but ones that would be equally useful in a scrappy startup environment.1. Get fluent in financeKnowing how a business makes money is crucial regardless of the size of the company and your role in it. Unfortunately, a lot of data science work often happens in silos where the problem statement or hypothesis, or analysis workflow is top-down and may lack direct alignment with the company’s financial goals.As you take on a more senior leadership role with the team, it is essential that you speak the language of business. Having a broad understanding of terms like CapEx vs. OpEx, EBITDA margin, amortization, blended CAC, churn cohorts, fair share index, etc. is helpful when you are communicating the results to the higher-ups. This way, you can tailor your insights to highlight how data science-driven initiatives will impact these areas, making your analysis more relevant and convincing to financial stakeholders.Did you know Apple spent $110 billion on stock buybacks in 2024. Why? Fewer shares in the market = higher earnings per share (EPS), which boosts the stock price.Knowing your numbers can benefit both you and the company: Understanding your numbers means that you know what’s working and not working for the business, identify areas for growth, and make sound financial choices based on data. For instance, instead of just showing improved model accuracy, one could demonstrate exactly how the predictions would impact the bottom line.Similarly, by showcasing how your work directly contributes to the company’s financial success, you can even negotiate better pay for yourself!But it goes beyond just communication. This knowledge opens doors to opportunities many data scientists miss. For instance, there are schemes that allow you to apply for tax rebates on your company’s CapEx that are associated with R&D activity (like patent-related costs, specialized software licenses, etc).I have seen teams who were able to secure funding by understanding these financial mechanisms and positioning their ML infrastructure investments as R&D initiatives.Likewise, there are certain government grants you or your company may be eligible for, depending on the space you are in. For instance, USDA (United States Department of Agriculture) offers grants and funding for projects in agri-tech innovation.How to build this skill?Read books on finance to quickly grasp key terms and learn from case studies of other companies in the same niche as you (worst case scenario — you either fail fast or best case scenario — you learn about common pitfalls to avoid).If you don’t have the time to read books end-to-end, at the very least, get familiarized with their key ideas. I use AcceleratEd to get book summaries but there are other options that you choose from that I have discussed in this article.P.S. Here is my book collection for upskilling in finance, including books like The Alchemy of Finance, Value Investing, and One Up On Wall Street.Consume content from YT channels like TheFinanceStoryteller and Investopedia who break down complex finance topics into bitesized chunks.Keep an eye out for bursaries and grants applicable to your business.Shadow your COOs, Operations Manager, or, in some cases, even your POs (mine has been god-sent in helping me understand value calculations in the healthcare sector and improving my corporate finance understanding).2. Keep Up With The CurveLove it or hate it, but you can’t deny the fact that the AI/ML/Generative AI field is moving at an unprecedented rate. I have often read news articles describing technology X replaced technology Y and I am left thinking — what is technology Y!On average, about 8000 new research papers (in Computer Science category) are published on arXiv every month! [Source]To provide any sort of thought leadership in this new role, your industry, and technological awareness need to operate at two levels — local and global.Keeping up with the local curve involves staying updated with the latest tools, techniques, and trends. In practical terms, this would translate as knowing (a) which models sit on top of the leaderboard for your usecase (be it forecasting, generative AI, or computer vision), (b) any new groundbreaking frameworks that would be game-changers for your field (for instance, Baidu recently unveiled iRAG technology that addresses the issue of hallucinations in image generation), and (c) advancements in DevOps/LLMOps/MLOps that could streamline workflows and improve efficiency.Keeping up with the global curve means acknowledging the bigger picture around the tech field— understanding how innovations are shaping industries and the broader ethical and societal impacts of these technologies — especially as governments around the world are taking steps to regulate the tech domain.In practical terms, this could mean keeping up-to-date with regulations in the field in which you operate (legal, healthcare, FMCG, etc) and checking compliance with relevant guidelines.For instance, the European Union’s AI Act 2024, which came into effect recently, has detailed guidelines on the dos and don’ts surrounding the development, deployment, and use of AI, including guidelines such as mandatory watermark to content generated by AI.Similarly, keeping track of the big tech players like NVIDIA, OpenAI, Anthropic, etc. is even more important to anticipate short and long-term technological shifts for your business. A short-term example would be the recent news of the OpenAI-Microsoft partnership turning sour, which could impact any ongoing projects if you rely on Microsoft’s Azure OpenAI as your LLM provider.A long-term example is the recent investment in nuclear power projects by companies like Microsoft, Amazon, and Google, to meet the growing demand for high energy consumption by large language models (LLMs), often seen as a bottleneck for AI advancements. A stable, predictable, and carbon-free energy source could mean long-term cost savings for your AI-driven business.How to build this skill?Get a daily dose of tech news via apps (like Curio) or websites like HackerNews.Subscribe to a couple of weekly AI newsletters, or as many as you can realistically keep up with given your workload. I am highly self-aware and my only go-to is The Batch.3. Be a communication ninjaFor a lucky few who step up from data scientist into this new leadership role, soft communication skills — useful for managing teams, data storytelling, and cross-team collaboration — come naturally to them. For the rest, there’s hope! With practice, achieving any skill is possible.And, before you ask why this is crucial — Imagine not knowing how to pitch your excellent data product to a group of non-technical VCs and investors. Or, an effective way to communicate insights from your week-long EDA process. Or, the right way to motivate your brilliant but overwhelmed data scientists during a critical product launch.Stepping into a leadership position means being firm but polite, clearly explaining what the team needs to do, and being crystal clear with stakeholders on technical limitations between their ask and what’s within the realm of possibilities — keeping in mind constraints like cost, latency, etc.It means staying cool when a stakeholder says ‘ChatGPT can do this in seconds’ or when someone demands ‘a 100% accurate model.’To deliver this effectively, you need to learn the different dynamics at play. You need to be more diplomatic and rational rather than reacting impulsively when someone suggests ‘trying these 20 ideas that came up during the meeting’ or using inappropriate verbal and non-verbal cues when you can clearly detect scope creep.How to build this skill?Again, books can be your best friend here. Here is my book collection for managing team dynamics, including books like Emotional Intelligence 2.0, The Five Dysfunctions of a Team, and Crucial Conversations: Tools for Talking When Stakes Are High, Made to Stick. I recently wrote about how these books have been insanely useful for saving my sanity as a tech lead.(Books can only take you so far so step up to) Lead stakeholder-facing meetings at work. Nothing beats a hands-on experience.Volunteer for roundtable discussions and fireside chats at conferences and seminars. These formats are more relaxed and take the pressure off compared to when you’re the only one presenting and others are passively listening. Back up your discussion points with facts and shreds of evidence from books, recent news, and reputed research papers to ensure your argument holds weight.Final ThoughtsIt might be scary to transition from a core data scientist role to a thought leader who is looked up to define, foresee, and shape the strategic direction of the data team.But you need to remember that, as a person with both tech and business skills in your arsenal, no one is better positioned than you to take the reins.You understand the technology behind the black box models, which makes finding feasible tech solutions easier, and you also have the business acumen to best align these solutions with business objectives.I hope this article was useful and should an opportunity present itself, you’ll grab it with both hands. Watching your company/team/BU (business unit) succeed and knowing you’ve played a part in their growth can be a truly rewarding experience.3 Business Skills You Need to Progress Your Data Science Career in 2025 was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.  office-hours, career-development, business-strategy, leadership, data-science Towards Data Science – MediumRead More

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