Career Path of a Senior Data Scientist at Newfront Insurance
Matthew's career journey began with an Informatics major, now data science, but early on found data science internships scarce, leading to a marketing analytics role at a startup for initial business experience; Later, a consulting internship at IBM, though tech-focused rather than data science, provided valuable real-world consulting exposure. After several roles involving SQL exposure and data analytics, Matthew landed at Newfront Insurance, where data science interests are applied, fulfilling the initial goal.
Data Science Career Path, Internship Experience, Consulting, SQL Proficiency, Industry Diversification
Advizer Information
Name
Job Title
Company
Undergrad
Grad Programs
Majors
Industries
Job Functions
Traits
Matthew Slodowitz
Senior Data Scientist
Newfront Insurance
University of Michigan
Mathematics, Data Science, Statistics
Insurance, Technology
Data and Analytics
Greek Life Member
Video Highlights
1. Early Career Exploration: Faced challenges finding data science internships in 2014 due to the field's early stage. Emphasized gaining general business experience as a primary goal, leading to a marketing analytics internship.
2. Strategic Internship Choices: Prioritized tech-focused experiences at IBM to pursue consulting aspirations, highlighting the importance of aligning internships with long-term career goals, even if the immediate project wasn't perfectly aligned with data science.
3. Skill Development and Industry Diversification: Emphasized SQL proficiency gained at Argus Information and Accenture, along with exposure to various industries (banking, insurance, public transit, pharma). Highlights the value of diversifying industry experience and continuously developing core data skills like SQL and data visualization tools.
Transcript
Could you walk me through your career path, starting with your experiences in college? Any internships or jobs did you have before your current role?
When I started at Michigan, I majored in informatics, which is now data science. I was looking for internships within the data science community back in 2014. It was an interesting time because data science was very early in its career stage, so not many people had data science internships or even analytics-focused internships.
My biggest priority when looking for internships was to gain experience. My first internship wasn't related to data science. I worked in a marketing role doing some analytics for a startup called Infront, based in Chicago.
It was a small startup, and I really just wanted some business world experience. They gave me an opportunity to work on data science projects and play around with their data sets. This gave me business world experience to put on my resume and allowed me to gain data science experience by exploring the available data.
That experience helped propel me into another internship my junior year at IBM. I went to IBM because it was very tech-focused, and I thought I could pursue more data science opportunities. My goal at the time was to work at a consulting firm, specifically in a role that was data science or data analytics heavy.
I interviewed with many consulting firms and analytics companies. I eventually received an offer from IBM to work as a consultant. This role was tech-focused, not data science-focused, and I didn't get to choose my projects; I worked on whatever was available.
My goal at IBM was to secure a full-time job there. The internship was great for gaining real consulting experience. I traveled for work, worked on a project with real consequences, and got excellent exposure to consulting.
After completing the internship, I didn't receive a return offer, so I had to scramble for a new internship. I applied to every company that had a data science opportunity. I was a bit stressed about finding a job after college, around 2015-2016.
I applied to many entry-level roles, ideally still looking for consulting positions in a data role or a general data role. I was graduating early in December instead of May, so I was working harder to find a position. I applied to many jobs and reached final rounds with some consulting firms.
When preferring certain jobs, the biggest factor for me was where I would be placed. I ended up getting a job at a company called Argus Information, located in White Plains, New York. This role was more data-centric and heavily SQL-focused, which is where I picked up most of my SQL skills.
I didn't have prior SQL experience before this role, and Argus involved a lot of banking-centric data work. I worked with banking clients in the deposit industry, focusing on checking, savings, and money market accounts. It still had consulting aspects and provided good exposure, as I worked with many different banks, including Canadian ones.
After about two years, I realized I needed a change. I didn't want to work at a small, hyper-focused banking company anymore. I wanted to work in consulting. So, I switched to Accenture, where I stayed for about three years.
At Accenture, I gained more SQL exposure and worked across various industries beyond banking, including insurance, public transit, and pharma. I also did more financial and financial consulting work. I learned a lot of SQL, how to use Power BI, and how to work effectively with stakeholders.
That experience at Accenture propelled me to my next role at Peloton Interactive, where I worked in data analytics, which was perfect for me. Peloton was a different conversation, with a lot of chaos during my year and a half there. However, I gained experience in supply chain, worked with legal, used Tableau, and got more exposure to different data projects.
This led me to my current role at Newfront, where I'm doing more financials and data science, which is what I truly wanted.