Post Galvanize

After completing the Galvanize Data Science Immersive (DSI) program, I moved to Chicago to live with my girlfriend. I had two goals: 1) launch PinkSlipper, my capstone project, as a full fledged business and 2) find a full-time job. In this post, I’ll describe my job search as well as explicitly state the offers I received.

Before I reveal the results, I must point out that I had a very strong background coming into Galvanize:

  • Strong academic record with PhD and 15+ authored papers
  • 2.5 years of work experience inventing and implementing billion-dollar patterning techniques for manufacturing computer chips at Intel
  • Strong math, stats, analytics, and programming background prior to Galvanize

I went about my job search on four vectors:

  1. Going to data science meetups and finding about opportunities through other data scientists
  2. Responding to recruiters who messaged me on Linked-In
  3. Actively connecting to people at organizations I was interested in
  4. Following up on leads from Galvanize

Of the four listed above, most my success came from proactively connecting with companies I wanted to work for as well as leads from Galvanize. To connect to the right person, I would ask strangers at networking events if they knew anyone at Company X. I would describe my background, mention I was interested in that company, and ask if they could put me in touch. This was much more successful than I ever thought.

Once I made contact, I initiated the interview process which consisted of:

  • Telephone contact / initial screen
  • Technical Screen
  • Takehome exam
  • On-site interview

The initial telephone screen was to gauge a candidate’s fit. I did not mold my thoughts based on the company I chatted with. I would simply state I have a strong background in stats, math, and programming with recent experience in machine learning and that my core strength was in experimentation, A/B testing, and simply making decisions with data to grow a business or product. If a recruiter mentioned they were looking for a data engineer, I would say “I have limited experience in that area, and cannot supply you the full value that a trained data engineer could. However, I suggest you can contact X for a good data engineer”.

During technical screens, I was asked to talk about a personal project, give a suggestion about how to complete a certain task, define various terms, or complete online programming challenges. All of these were quite easy given both my work background as well as the preparation from the Galvanize program.

I completed 5 takehome exams during my interview process. Every one of them led to a final round. There is zero chance I could ever have completed these takehomes without entering the Galvanize program, and I was surprised to find out from the companies how well I did on them. The takehomes often asked some stats questions, dataframe manipulation tasks, SQL, and obviously a modeling problem (most often linear or logistic regression worked quite well though I would often show-off with more complex models to demonstrate my abilities).

Finally, in my on-site interviews, I was mainly asked about prior experiences and to see if I was a good culture fit. A number of companies asked basic analytic problems which were much easier than I prepared for coming out of Galvanize. I aced every coding problem I was given and thought these interviews were much easier than I expected. On a side note, I was interviewing only in Chicago which I believe is a much less rigorous process than in SF.

My job search came in two phases. The first phase consisted of two companies I was interested in. I completed most the interview process while in the Galvanize DSI program. I did final rounds at both when I got back to Chicago and obtained an offer from one. With no other offers, and the company unwilling to negotiate, I rejected the offer as it was well below what I could accept.

I then initiated a second phase which consisted of building a job opportunity pipeline with as many interesting opportunities I could find. I used prior Galvanize Alum Greg Kamradt's (now data scientist at Salesforce) Interview tips and tracker at Lessons Learned Data Science Interviews which has a fantastic lecture here:

Before I run through the details of the process and offers, here are some stats regarding my interview funnel:

  • 18 companies contacted
  • 13 phone screens
  • 5 takehomes (not all companies required this though)
  • 7 on-sites
  • 5 offers
  • Total time / effort – 2 months

Below are the offers I received. I have masked the company names, but supplied details many of you will be interested in. Note that the 190k base was the highest offer ever obtained by a Galvanize DSI fellow. The first three offers were more inline with other members of my cohort who had strong backgrounds and work experience.

Title

Base

Bonus

Estimated Total Value of Offer

Company A

Data Scientist

110k

None

110k

Company B

Data Scientist

125k

None

135k

Company C

Principal Data Scientist

120k

10k

150k

Company D

Senior Data Scientist

155k

20k

180k

Company E

Principal Data Scientist

190k

40k

230k

Making the decision actually became one of the most challenging parts of this process for me. I chatted with Katie Kent (Director of Outcomes at Galvanize) for advice. By the way, she is awesome and one of the best reasons to attend Galvanize over other data science programs. She suggested 5 vectors that are most critical for success in a role:

  1. Opportunity for growth
  2. People that I work with
  3. The person I report to
  4. Day to day work
  5. Exit Opportunity

For each company, I rated each vector from 1 to 5 and included an additional vector for compensation. I had a threshold compensation which I rejected any offer that was below, and my final decision was based on the sum of the various ratings. In the end, I decided to work at Trunk Club. I saw a huge opportunity to contribute to the data science effort and the group was small so I could own full implementations of improved analytic algorithms. Culture and fit was amazing and I’m looking forward to beginning!