Seven keys to success in your data science job

This blog post has been updated and was originally authored by Metis Sr. Data Scientist Jonathan Balaban.

 

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 favourite things. During this timeframe, I started several new roles myself in diverse cities and corporate cultures. I wish I knew these seven insights when starting my career. While not comprehensive, these are simple, yet powerful, ways 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 don’t want to slide in silently either. Remember, you’re not a spy infiltrating a foreign corporation. Your new firm’s investing in expanding their data science team, which I’m guessing it’s a high-priority initiative.

If you’re the first data scientist in the building or if your team is small, let people know you and your troops exist. Build 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 relationships.” – Kathy Ireland

 

2. Find allies

It’s part of your manager’s job description to support and enable your best work, yet it’s not enough to rely on one person. Your manager may be too busy, disengaged, disincentivised or there may be a personality clash.

As a data scientist, you’ll need allies who support your requests for key data and software, who’ll influence the decision-makers to consider your findings, and who can assess your results. Look for experienced colleagues who can mentor you in your new role and guide you to maximum impact.

 

3. Be honest

It can be tempting to oversell yourself and your skill set to your new employer. You may have started it all during the interview phase because you desperately wanted or needed the job. Whatever the circumstance, it’s essential to be realistic to oneself and the wider team. This is especially true if you’re the lone data scientist with no one to scrutinise you.

I emphasise margin to my students because we’re not great 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; this is the guiding principle for many of the most successful firms and leaders. Data science is powerful, but it can’t fix everything.

 

4. Do homework

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

It’s better to exercise patience and confirm 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.

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 shape the way you view individuals.

It’s best to steer away from these obstacles. 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 prioritising potentially ambiguous tasks and clear the signal from the noise. Make room for continuing education. The data science field is ever-evolving, so you should stay current on packages and developments.

Why not use the data science tools at your disposal to track progress? Github’s Activity Dashboard’s 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 are fantastic opportunities to start with a clean slate and streamline your processes, incorporating better habits and organisation to the work you do. Maybe you’ve witnessed new integrations to your stack or a new you’d like to try.

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. Bring due-diligence to these new platforms, so they don’t secretly sabotage your work. Be sure to your leadership which platforms are business-mandated and user preference.

 

These simple, yet valuable, tips can help support you to accelerate your professional development and get a head when starting your data science career. Have other tips? Share them below to inspire others on their data science career journey!