If you've been a data scientist for a while, sooner or later you'll notice that your day-to-day life has gone from being a VSCode-loving, research-paper-reading, git-release committed data scientist to a data scientist who drives collaboration. , individual who defines the scope of the project, managing stakeholders and establishing strategies.
This change will be gradual and almost imperceptible, but will require you to take on different roles to ensure data initiatives are on track and impactful. It is at this point that you will start to notice the need to hone some business skills in addition to your usual data science skills. This will also be a good indication that you are ready to pursue senior technology leadership positions, such as director, leader, or DS staff.
Here are my top three picks that have been quite useful when taking on a data science leadership role at a FTSE 100 company, but would be equally useful in a challenging startup environment.
Knowing how a company makes money is crucial, regardless of the size of the company and your role in it. Unfortunately, much data science work is often done in silos where the problem statement, hypothesis, or analysis workflow is top-down and may lack direct alignment with the company's financial objectives. .
As you take on a more senior leadership role on the team, it is essential that you speak the language of business. Have a broad understanding of terms such as CapEx vs. OpEx, EBITDA margin, amortization, combined CAC, churn cohorts, fair share ratio, etc. It is useful when communicating results to superiors. This way, you can tailor your insights to highlight how data science-driven initiatives will impact these areas, making your analysis more relevant and compelling to financial stakeholders.
Did you know that Apple spent $110 billion on share buybacks in 2024? Because? Less shares on the market = higher earnings per share (EPS), which increases the share price.
Knowing your numbers can benefit you and the company: Understanding your numbers means you know what is and isn't working for the business, identify areas of growth, and make sound financial decisions based on data. For example, instead of simply showing increased model accuracy, one could demonstrate exactly how the predictions would affect the final result.
Similarly, by showing how your work directly contributes to the company's financial success, you can even negotiate a better salary for yourself!
But it goes beyond mere communication. This knowledge opens doors to opportunities that many data scientists miss. For example, there are schemes that allow you to claim tax refunds on your company's CapEx associated with R&D activity (such as costs related to patents, specialized software licenses, etc.).
I have seen teams that were able to obtain funding by understanding these financial mechanisms and positioning their ML infrastructure investments as R&D initiatives.
Likewise, there are certain government subsidies You or your business may be eligible, depending on the space you are in. For example, the USDA (United States Department of Agriculture) offers grants and funding for agricultural technology innovation projects.
How to develop this skill?
- Read books on finance to quickly understand key terms and learn from case studies of other companies in the same niche as you (at worst, you will either fail quickly, or at best, you will learn about common mistakes that you should avoid).
If you don't have time to read books cover to cover, at least familiarize yourself with their key ideas. I use <a target="_blank" class="af qy" href="http://tedapp.ai” rel=”noopener ugc nofollow” target=”_blank”>Accelerated for book summaries, but there are other options you can choose from that I've reviewed in this article.
PS Here is my <a target="_blank" class="af qy" href="https://tedapp.ai/collections/tkyjw” rel=”noopener ugc nofollow” target=”_blank”>book collection to improve your finance skills, including books like <a target="_blank" class="af qy" href="https://tedapp.ai/book/the-alchemy-of-finance-i477623674″ rel=”noopener ugc nofollow” target=”_blank”>The alchemy of finance, value investment, and <a target="_blank" class="af qy" href="https://tedapp.ai/book/one-up-on-wall-street-i186585127″ rel=”noopener ugc nofollow” target=”_blank”>One up on Wall Street. - Consume content from YouTube channels such as The Finance Storyteller and investopedia that break down complex financial topics into bite-sized chunks.
- Be on the lookout for scholarships and grants applicable to your business.
- Follow your COOs, COOs, or in some cases even your COOs (mine has been a godsend in helping me understand value calculations in healthcare and improving my understanding of corporate finance).
Love it or hate it, but you can't deny the fact that the field of ai/ML/Generative ai is moving at an unprecedented pace. I've often read news articles describing technology x replacing technology Y and I'm left wondering: what is technology Y?
On average, around 8000 new research articles (in the Computer Science category) are published on arXiv every month. (Fountain)
To provide any type of thought leadership in this new role, your industry and technological knowledge must operate on two levels: local and global.
Keep up to date with local curve It involves staying up to date with the latest tools, techniques and trends. In practical terms, this would translate into knowing (a) which models are on top of the leaderboard for your use case (be it forecasting, generative ai, or computer vision), (b) any new, innovative framework that can be a game-changer for your field (e.g. Baidu recently introduced iRAG technology which addresses the problem of hallucinations in image generation) and (c) advances in DevOps/LLMOps/MLOps that could streamline workflows and improve efficiency.
Keep up to date with global curve It means recognizing the bigger picture around the technological field (understanding how innovations are shaping industries and the broader ethical and social impacts of these technologies), especially now that governments around the world are taking steps to regulate the technological domain.
In practical terms, this could mean staying up to date with regulations in the field you operate in (legal, healthcare, consumer goods, etc.) and checking compliance with relevant guidelines.
For example, the <a target="_blank" class="af qy" href="https://commission.europa.eu/news/ai-act-enters-force-2024-08-01_en” rel=”noopener ugc nofollow” target=”_blank”>European Union ai Law of 2024which recently came into effect, has detailed guidelines on the do's and don'ts around the development, deployment and use of ai, including guidelines such as mandatory watermarking for ai-generated content.
Similarly, keep track of big tech players like NVIDIA, OpenAI, Anthropic, etc. It is even more important to anticipate short- and long-term technological changes for your business. A short-term example would be the recent news of the <a target="_blank" class="af qy" href="https://www.nytimes.com/2024/10/17/technology/microsoft-openai-partnership-deal.html” rel=”noopener ugc nofollow” target=”_blank”>The OpenAI-Microsoft partnership is turning sourwhich could impact any ongoing projects if you rely on Microsoft's Azure OpenAI as your LLM provider.
A long-term example is the recent <a target="_blank" class="af qy" href="https://www.nytimes.com/2024/10/16/business/energy-environment/amazon-google-microsoft-nuclear-energy.html” rel=”noopener ugc nofollow” target=”_blank”>investment in nuclear energy projects by companies such as Microsoft, amazon and Google, to meet the growing demand for high-power large language models (LLMs), often seen as a bottleneck for ai advancements. A stable, predictable, carbon-free energy source could mean long-term cost savings for your ai-powered business.
How to develop this skill?
- Get a daily dose of tech news through apps (like <a target="_blank" class="af qy" href="https://www.rionews.ai/purpose” rel=”noopener ugc nofollow” target=”_blank”>Curiosity) or websites like HackerNews.
- Subscribe to a couple of ai's weekly newsletters, or as many as you can realistically keep up with given your workload. I am very self conscious and my only recourse is <a target="_blank" class="af qy" href="https://www.deeplearning.ai/the-batch/” rel=”noopener ugc nofollow” target=”_blank”>The lot.
For the lucky few who transition from data scientists to this new leadership role, soft communication skills (useful for managing teams, data storytelling, and cross-team collaboration) comes naturally to them. For the rest, there is hope! With practice, achieving any skill is possible.
And, before you ask why this is crucial, imagine not knowing how present your excellent data product to a group of non-technical investors and venture capitalists. Or an effective way to communicate ideas of your week-long EDA process. Or the correct way to motivate your brilliant but overwhelmed data scientists during a critical product launch.
Taking a leadership position means being firm but polite, clearly explaining what the team needs to do, and being very clear with stakeholders about the technical limitations between their requests and what is within the realm of possibility, taking into account limitations such as cost, latency, etc. .
It means staying calm when a stakeholder says “ChatGPT can do this in seconds” or when someone demands “a 100% accurate model.”
To achieve this effectively, you need to learn the different dynamics at play. You should be more diplomatic and rational instead of reacting impulsively when someone suggests 'testing these 20 ideas that came up during the meeting' or using inappropriate verbal and non-verbal cues when a reach shift can be clearly detected.
How to develop this skill?
- Once again, books can be your best friend here. here is my <a target="_blank" class="af qy" href="https://tedapp.ai/collections/qjdnl” rel=”noopener ugc nofollow” target=”_blank”>book collection to manage team dynamics, including books like Emotional Intelligence 2.0, The five dysfunctions of a team and crucial conversations: tools for talking when the stakes are high, made to last. I recently wrote about how these books have been incredibly helpful for <a target="_blank" class="af qy" href="https://medium.com/code-like-a-girl/5-practical-leadership-quotes-that-saved-my-sanity-as-a-woman-tech-lead-99fe3f1b53de” rel=”noopener”>saving my sanity as a technology leader.
- (Books can only take you so far, so skip ahead to) Conduct business meetings with stakeholders. There is nothing better than a hands-on experience.
- Volunteer at roundtables and informal talks at conferences and seminars. These formats are more relaxed and relieve pressure compared to when you are the only one presenting and others passively listen. Support your talking points with facts and snippets of evidence from books, recent news, and reputable research articles to ensure your argument carries weight.
