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7 Keys to Success in Your New Data Science Job (by Metis Sr. Data Scientist Jonathan Balaban)

By Jonathan Balaban • October 10, 2018


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This post was written by Metis Sr. Data Scientist Jonathan Balaban.

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Over the past decade, I’ve helped more than 100 students, colleagues, and friends start new careers and find fulfilling jobs. Seeing alignment in my friends’ professional lives is one of my very favorite things! During this timeframe, I started a number of new roles myself in diverse cities and corporate cultures. I wish I knew these 7 maxims when starting my career. While not comprehensive, and not necessarily sorted, these are simple but powerful keys that can help you make a great first impression and accelerate your professional development.

1. Get Noticed

While I don’t advise “coming into your new job like a wrecking ball,” you do not want to slide in silently either. Remember, you’re not a spy infiltrating a foreign corporation. Your new firm is investing in building out their data science team, and I’m guessing it’s a high-priority initiative.

So, especially if you’re the first data scientist in the building, or if your team is small, let people know that you and your troops exist! Forge relationships with other departments and when the time comes for you to ask for data, or for said departments to ask for analysis, you’ll be on a first-name basis!

Dear human, this will hold true until the robots take over: 

In business, it’s about people. It’s about relationshipsKathy Ireland

2. Find Allies

It’s part of your manager’s job description to support and enable your best work (and by the way, that goes in both directions). Yet, it’s not enough to rely on one person. Your manager may be too busy, disengaged, disincentivized, or there may be a personality clash.

As a data scientist, you need allies who will support your requests for key data and software, who will influence the decision-makers to consider your findings, and who can vet your results. Look for veterans who can mentor you in your new role and guide you to maximum impact!

3. Be Honest

It can be so very tempting to oversell yourself and your skillset to your new employer. You may have started it all during the interview phase because you desperately wanted or needed the job, so it’s hard to course-correct. Whatever the circumstance, it’s essential to be realistic to oneself and to the wider team. This is especially true if you’re the lone data scientist with nobody to vet you.

I emphasize margin to my students because we stink at estimating timelines on complex tasks. Remember Hofstadter’s Law: everything in data science takes twice as long as you think it will!

Also, remember to set realistic expectations and strive to over-deliver. We enjoy unexpected surprises over unexpected disappointments every single time, and this is the guiding principle for many of the most successful firms and leaders. Data science is powerful, but it can’t fix everything, everywhere.

4. Do Homework

This can be rephrased as “don’t make assumptions." It can be tempting — especially if you’re coming from a similar industry or role — to try and make your mark quickly by speaking with authority.

However, to prevent “foot-in-mouth” syndrome, it’s better to exercise patience and confirm that the new company uses the same systems, processes, metrics, business rules, and ethos before applying past strategies to your new role. Your allies can be the perfect sounding-board for this confirmation.

5. Evade Drama

In certain firms, drama may exist…

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Funny joke out of the way, if you’re the “new person," certain cliques may be quick to recruit you to their side. Even listening to office gossip can color the way you view individuals. Remember, there are always two sides to every story, but the primacy effect is powerful.

It’s best to steer away from these obstacles; you’ll give everyone the benefit of the doubt while being much more effective with your time. As scientists, our objectivity — and the perception of it — is a key aspect of our influence.

6. Plan Development

With the help of your manager and allies, build a qualitative plan for the upcoming quarter and year with goals and objectives. These will guide you in prioritizing potentially ambiguous tasks and clear the signal from the noise. Make room for continuing education; the data science field is ever-evolving and you should stay current on packages and developments.

And hey, why not use the data science tools at your disposal to track progress? Github’s Activity Dashboard is great for documenting file activity over time, and I use RescueTime to see where my mental focus is spent over the course of the week.

7. Get Systematic

New jobs, like New Year's and big moves, are fantastic opportunities to start with a clean slate, incorporating better habits and organization to the work you do. Maybe you’ve witnessed new integrations to your stack or a new IDE you’d like to try. Oftentimes, the reduced workload (assuming you’re not drowning in HR paperwork) as you ramp up at a new job could be the perfect opportunity to streamline your processes.

Just be warned: don’t bite off more than you can chew. While it’s tempting to overhaul everything, don’t let it distract you from providing value and keeping focused. To the Do Homework point, bring due-diligence to these new platforms so they don’t secretly sabotage your work. And ask your leadership which platforms are business-mandated and which are user preference.

There we go; 7 simple but powerful keys for acing your first month on the job. Any other data science or general job tips I missed? Share with me on Twitter at @ultimetis.


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