Work in data science? Want to have a job next year? Read this.

Welcome to another edition of “In the Minds of Our Analysts.”

At System2, we foster a culture of encouraging our team to express their thoughts, investigate, pen down, and share their perspectives on various topics. This series provides a space for our analysts to showcase their insights.

All opinions expressed by System2 employees and their guests are solely their own and do not reflect the opinions of System2. This post is for informational purposes only and should not be relied upon as a basis for investment decisions. Clients of System2 may maintain positions in the securities discussed in this post.

Today’s post was written by Kevin Nutter.

Building an indispensable data science department requires much more than great data science. Contrary to an in-house data science team, which only needs to build one successful data science department, at System2, we build a new department for every client. We’ve landed on five things that must happen to effectively manage and develop a data science department:

  1. Prioritize - actionable analyses above informative ones.

  2. Track - the time the data science team spends on different analyses.

  3. Score - ask the client’s team for a score on each analysis.

  4. Present - communicate to the senior stake holders the scores and efficiency.

  5. Iterate - optimize the data science team’s time on the activities with the most impact.

I’ll dive into each of these steps in more detail through 2024. For today, we’ll start with the most difficult step, how to prioritize data science analyses.

This can be very difficult to do when many asks we receive are open-ended and the outcome is uncertain. We use our prioritization matrix to decide what to work on, in what order and how to communicate it to our client’s point person. Let's look at it for a case where the stakeholders are at a client that's a hedge fund.

Prioritization Matrix

The matrix is a table split into four quadrants representing a certain value to the client based on two concerns: technical difficulty (or time required by the data science team) vs. the potential impact on the fund. Within each quadrant, we order the activities by the investment portfolio position size or potential position size, for which we need the investment team’s input.

Priorities are always alive, but a weekly call should be scheduled with the investment team point person to provide them with an updated prioritization matrix and the top things we’re working on in the upcoming week.

Low vs. High Complexity

One of the most difficult things to teach a Junior Data Scientist is what’s easy vs. hard. At first glance, everything seems easy. Only after years of failure can one accurately identify what they know vs. what they don’t know, and reset their (and the investment team’s) expectations accordingly.

Here’s a few distinctions and expectations we set for the two categories.

A good rule of thumb is that a Senior Data Scientist paired with a Junior Data Scientist should be able to publish 100 analyses a year.

Remember, these are the keys to having a job in data science next year. Prioritize accordingly. Look for the next piece on tracking time spent in early 2024. Have a great new year!

matei zatreanu