Lyft data scientist shares five pieces of career advice

Troy recommends reading Storytelling With Data, and the Chartify python library for those who want to improve their data visualization skills.

Finally, look out for opportunities to collaborate with UX researchers.

Whether that’s by partnering on a project together or leveraging each others’ existing work.

Including joint quantitative and qualitative insights in your story can make for a powerful combination.


Stakeholder ManagementWorking with various stakeholders means being able to take on their perspective.

For example, if you’re working with a Product Manager, showing some product sense can be a sure way of building trust.

Another technique that can be useful is taking the perspective of users, and leveraging human-centered design principles.

A trap to avoid when working with stakeholders is going down a rabbit hole, and coming out with a polished deliverable.

This approach can easily backfire if it turns out that the work you’ve done is not what the stakeholder had in mind.

The better approach is to be iterative and incremental.

For example, leveraging lean startup methodology to build initial prototypes and getting feedback from stakeholders to make sure you’re on the right track.

Finally, be direct with recommendations, which should be concrete and clear so that the stakeholder can assess the options and make a decision.

Try not to overwhelm them with the nitty-gritty analysis.

Instead, opt to put that information in an appendix or omit it all together so that you can focus on the key elements of your recommendation.


Managing UpOne of the most important relationships that you’ll manage as a data scientist is the one with your boss.

It’s important to learn the art of managing up so that you can set yourself up for success.

Listening more than you speak, and making your manager feel heard is a great way to build rapport.

There’s nothing worse for your relationship with your manager than getting their advice, only to do the complete opposite of what they recommended.

Figuring out what your manager is evaluated on, and then helping your manager look good is one of the best ways to build a good relationship with them.

Ultimately the fate of your career rests in the hands of your manager, so be sure to find out what motivates them, and how to best gel with their management style.


How to Build a ConsultancyFor those looking to take on part-time client work or to build a full-time consultancy, Troy has a variety of recommendations.

The first is to be disciplined about setting your work hours and creating processes for yourself.

This might mean renting out a coworking space where you can simulate a traditional work environment.

Sticking to a schedule similar to salaried employees can help with building positive momentum and establishing a positive routine.

Another recommendation is to hone your ability to pitch and sell.

A large portion of your time when you’re starting out will be trying to find client work.

As you grow your reputation, you may start to receive inbound but in the initial stages, there will be a lot of legwork to get the first few projects.

When sourcing clients, the three main approaches are personal networks, community networks, and online portals such as Upwork.

Your personal network is likely where you’ll have the most success in obtaining client work, whether it’s past colleagues or your college alumni network.

The community network consists of physical communities such as those found on Meetup, or online communities such as Reddit.

In the latter case, Troy was able to find some client work after doing a quick one-off analysis for someone on a Reddit data science community.

The third approach is the less lucrative of the three, as you’ll be competing with a global freelance talent base, many of which have no issue with putting in low-bids given their low cost of living.

Data Minds is a series that profiles professionals working with data.

In this series, you’ll learn about their story, day-to-day, and advice for others.

Previous interviews include data scientists from Red Bull, Open Door, Snapchat and Netflix.

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