One Year as a Data Scientist at Simple

This article is originally published at http://jasdumas.github.io

It has been one year since joining the data science team at Simple. Although there have been changes in my role, my team and the company, overall its been a life changing experience filled with opportunity. I work remotely from my home and sometimes a local coffee shop, which has been extremely positive for my work/life balance. (I’m currently in Portland this week for the quarterly Thing Week with the rest of the remote employees)

Data Science is an evolving field and while I’m still satisfied making the leap from Engineering, I’d like to share some things about my experience over the past year for anyone interesting in working in applied data science or for tech companies.

Some things I do/did:

• :question: Ask and answer questions using data: As a technology company in the finance world we have a chance to learn about our customers through data generated from the product. Everyone is driven to understand how our customers use the product and how we can make the product better. I’ve spent a majority of my time implementing analytics techniques to help answer those types of questions. There are some really smart folks who work to build and support the data pipeline that enables scientists & analysts to reliably access the data though SQL querying. Answering these questions can sometimes help inform strategic changes in the product and other times the answers will lead to more questions and the development of testable hypotheses.

• :thought_balloon: Come up with new ideas: Performing analytics tasks usually gives me a good starting point for ideation on new product features. Through research and applying statistical techniques (i.e. inference) I’ve had a few opportunities to infuse data into our newest on-boarding flow to help customers find value quicker through a guided account setup.

• :microscope: Experiment with new ideas: Creating data-informed products is really interesting but sometimes we want to test many ideas out and discover the better solution through experimentation. In my previous role as a Data Scientist in the insurance industry, I did not have experience with A/B testing and spent time learning from my more experienced colleagues at Simple about study design, instrumentation and how to measure the performance of experiments. This is one area of growth that I’m really excited about and proud to help lead at Simple, as it has helped me practice important skills in explaining technical topics to non-technical folks for product recommendations.

• :busts_in_silhouette: Collaborate and communicate ideas: I get a lot of exposure to collaborative brainstorming through meetings, discovery sessions, and peer reviewing issues & pull requests which all build on effective communication and good team health. I frequently share what I’m working on with my functional data science team, my dedicated product team, and even the entire company during all hands meetings.

I’m sure this what I look like when giving presentations

Some things I learned/learning:

• :dancers: My team trusts me and I trust them: Trust is an important feature of productive work environments, which is earned. I work remotely which means not only do I get to eat lunch at home with my :dog: but large portions of my day are self-directed and organized around the projects I take on. In that setting, I can fully dive into debugging code or researching a technique to apply, but some of the most effective solutions have come from leveraging my colleagues by jumping on a video chat to sort out a challenging problem.

• Not every problem needs an academic solution: I have invested in my data science career with graduate-level courses, MOOCs, books and conferences. It’s often difficult to practice data analysis on industry data since it can be filled with sensitive information, so the art of selecting the best tool and technique usually comes from on the job experience. Utilizing MacGyver techniques with my data science toolkit, I’m learning how to better devise solutions that blend theoretical and practical knowledge.

• :rabbit2: Tech moves fast: I have worked for a few companies since graduating college, but nothing compares to the pace of working for an technology company. Some of the benefits of working in fast pace environment includes the ability to validate assumptions and iterate to develop new solutions which often results in getting to the see the success of a launched experiment or feature.

• :smile: Being comfortable to be myself: I’ve never worked for an organization that made me feel comfortable to be myself, more like Simple. Tech companies in general have some disparaging statistics around diversity and pay equity but working at Simple has made me feel empowered and opened me up to a community of individuals who add to my reasons for turning on my computer each day!

:tada: Despite all the growing pains, I have really appreciated being at Simple! If you are considering joining a tech company as a Data Scientist from an “un-traditional” background like I did, I recommend going for it!

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This article is originally published at http://jasdumas.github.io
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