Advice for data scientists and managers
Over the last 10 years, I have worked in analytical roles in a number of companies, from a small Fintech startup in Germany to high-growth pre-IPO scale-ups (Rippling) and big tech companies (Uber, Meta).
Each company had a unique data culture and each role came with its own challenges and a set of hard-earned lessons. Below, you’ll find ten of my key learnings over the last decade, many of which I’ve found to hold true regardless of company stage, product or business model.
1. You need to tell a story with data.
Think about who your audience is.
If you work in a research-focused organization or you are mostly presenting to technical stakeholders (e.g. Engineering), an academic “white paper”-style analysis might be the way to go.
But if your audience are non-technical business teams or executives, you’ll want to make sure you are focusing on the key insights rather than getting into the technical details, and are connecting your work to the business decisions it is supposed to influence. If you focus too much on the technical details of the analysis, you’ll lose your audience; communication in the workplace is not about what you find interesting to share, but what the audience needs to hear.
The most well-known approach for this type of insights-led, top-down communication is the Pyramid Principle developed by McKinsey consultant Barbara Minto. Check out this recent TDS article on how to leverage it to communicate better as a DS.
2. Strong business acumen is the biggest differentiator between good and great data scientists.
If you are a Senior DS at a company with a high bar, you can expect all of your peers to have strong technical skills.
You won’t stand out by incrementally improving your technical skillset, but rather by ensuring your work is driving maximum impact for your stakeholders (e.g. Product, Engineering, Biz teams).
This is where Business Acumen comes into play: In order to maximize your impact, you need to 1) deeply understand the priorities of the business and the problems your stakeholders are facing, 2) scope analytics solutions that directly help those priorities or address those problems, and 3) communicate your insights and recommendations in a way that your audience understands them (see #1 above).
With strong Business Acumen, you’ll also be able to sanity check your work since you’ll have the business context and judgment to understand whether the result of your analysis, or your proposal, makes sense or not.
Business Acumen is not something that is taught in school or DS bootcamps; so how do you develop it? Here are a few concrete things you can do:
- Pay attention in the Company All Hands and other cross-team meetings when strategic priorities are discussed
- Practice connecting these priorities to your team’s work; during planning cycles or when new projects come up, ask yourself: “How does this relate to the high-level business priorities?” If you can’t make the connection, discuss this with your manager
- When you are doing an analysis, always ask yourself “So what?”. A data point or insight only becomes relevant and impactful once you can answer this question and articulate why anyone should care about it. What should they be doing differently based on this data?
The ultimate goal here is to transition from taking requests and working on inbound JIRA tickets to being a thought partner of your stakeholders that shapes the analytics roadmap in partnership with them.
3. Be an objective truth seeker
Many people cherry pick data to fit their narrative. This makes sense: Most organizations reward people for hitting their goals, not for being the most objective.
As a Data Scientist, you have the luxury to push back against this. Data Science teams typically don’t directly own business metrics and are therefore under less pressure to hit short-term goals compared to teams like Sales.
Stakeholders will sometimes pressure you to find data that supports a narrative they have already created in advance. While playing along with this might score you some points in the near term, what will help you in the long term is being a truth seeker and promoting the narrative that the data truly supports.
Even if it is uncomfortable in the moment (as you might be pushing a narrative people don’t want to hear), it will help you stand out and position you as someone that executives will approach when they need an unfiltered and unbiased view on what’s really going on.
4. Data + Primary Research = ❤️
Data people often frown at “anecdotal evidence”, but it’s a necessary complement to rigorous quantitative analysis.
Running experiments and analyzing large datasets can give you statistically significant insights, but you often miss out on signals that either haven’t reached a large enough scale yet to show up in your data or that are not picked up well by structured data.
Diving into closed-lost deal notes, talking to customers, reading support tickets etc. is sometimes the only way to uncover certain issues (or truly understand root causes).
For example, let’s say you work in a B2B SaaS business. You might see in the data that win rates for your Enterprise deals are declining, and maybe you can even narrow it down to a certain type of customer.
But to truly understand what’s going on, you’ll have to talk to Sales representatives, dig into their deal notes, talk to prospects etc.. In the beginning, this will seem like random anecdotes and noise, but after a while a pattern will start to emerge; and odds are, that pattern did not show in any of the standardized metrics you are tracking.
5. If the data looks too good to be true, it usually is
When people see a steep uptick in a metric, they tend to get excited and attribute this movement to something they did, e.g. a recent feature launch.
Unfortunately, when a metric change seems suspiciously positive, it is often because of data issues or one-off effects. For example:
- Data is incomplete for recent periods, and the metric will level out once all data points are in
- There is a one-time tailwind that won’t sustain (e.g. you see a boost in Sales in early January; instead of a sustained improvement to Sales performance, it’s just the backlog from the holiday period that is clearing up)
Don’t get carried away by the excitement about an uptick in metrics. You need a healthy dose of skepticism, curiosity and experience to avoid pitfalls and generate robust insights.
6. Be open to changing your mind
If you work with data, it’s natural to change your opinion on a regular basis. For example:
- You recommended a course of action to an executive, but have lost faith that it’s the right path forward since you got more data
- You interpreted a metric movement a certain way, but you ran an additional analysis and now you think something else is going on
However, most analytical people are hesitant to walk back on statements they made in the past out of fear of looking incompetent or angering stakeholders.
That’s understandable; changing your recommendation typically means additional work for stakeholders to adjust to the new reality, and there is a risk they’ll be annoyed as a result.
Still, you shouldn’t stick to a prior recommendation simply out of fear of losing face. You won’t be able to do a good job defending an opinion once you’ve lost faith in it. Leaders like Jeff Bezos recognize the importance of changing your mind when confronted with new information, or simply when you’ve looked at an issue from a different angle. As long as you can clearly articulate why your recommendation changed, it is a sign of strength and intellectual rigor, not weakness.
Changing your mind a lot is so important. You should never let anyone trap you with anything you’ve said in the past. — Jeff Bezos
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7. You need to be pragmatic
When working in the Analytics realm, it’s easy to develop perfectionism. You’ve been trained on scientific methods, and pride yourself in knowing the ideal way to approach an analysis or experiment.
Unfortunately, the reality of running a business often puts severe constraints in our way. We need an answer faster than the experiment can provide statistically significant results, we don’t have enough users for a proper unbiased split, or our dataset doesn’t go back far enough to establish the time series pattern we’d like to look at.
It’s your job to help the teams running the business (those shipping the products, closing the deals etc.) get things done. If you insist on the perfect approach, it’s likely the business just moves on without you and your insights.
As with many things, done is better than perfect.
8. Don’t burn out your Data Scientists with ad-hoc requests
Hiring full-stack data scientists to mostly build dashboards or do ad-hoc data pulls & investigations all day is a surefire way to burn them out and have churn on the team.
Many companies, esp. high-growth startups, are hesitant to hire Data Analysts or BI folks specifically dedicated to metric investigations and dashboard building. Headcount is limited, and managers want flexibility in what their teams can tackle, so they hire well-rounded Data Scientists and plan to give them the occasional dashboarding task or metrics investigation request.
