Your First 90 Days as a Data Scientist

Source: Towards Data Science

I — Build Connections

Before anything else, let me start with building connections. When I was at school, I pictured data scientists as people spending all day long heads‑down writing code and building models. However, as I became more senior, I realized that data scientists make real impact by embedding themselves deeply in the business, using data to identify opportunities, and driving business decisions. This is especially true today with tighter DS headcount and AI automating basic coding and analysis workflows.

Therefore, building connections and earning a seat at the table should be a top priority during onboarding. This includes:

• Frequent onboarding sessions with your manager and onboarding buddy.
  These are the people who best understand your future scope, expectations, and priorities. In my case, my manager was my onboarding buddy, and we met almost daily during the first two weeks. I always came with a prepared list of questions I encountered during onboarding.

• Set up meet‑and‑greet calls with cross‑functional partners.
  Here is the agenda I usually follow in those calls:

  1. Personal introductions
  2. Their focus area and top priorities
  3. How my team can best support them
  4. Any onboarding advice or “things I should know”

  I especially like the last question because it consistently provides great insights. Five years ago, when I onboarded at Brex, I asked the same question and summarised the responses into categories here. The best I got this time is:

  Don’t be afraid to ask dumb questions. Play the new‑hire card as much as possible in the first three months.

• For those key partners, set up weekly/bi‑weekly 1:1s and get yourself added to recurring project meetings. You may not contribute much at first, but just listening in and collecting context and questions is helpful.

• If you are onboarding as a manager like me, you should start talking to your direct reports early. During onboarding, I aim to learn three things from my direct reports:

  1. Their projects and challenges
  2. Their expectations of me as a manager
  3. Their career goals

  The first helps me ramp up on the area. The latter two are critical for establishing trust and a collaborative working relationship early on.

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II — Build Domain Context

Data scientists succeed when they understand the business well enough to influence decisions—not just analyse outcomes. Therefore, another priority during onboarding is to build your domain knowledge. Common strategies include talking to people, reading docs, searching Slack, and asking a lot of questions.

I usually start with conversations to identify key business context and projects. Then I dig into relevant docs in Google Drive or Confluence, and read Slack messages in project channels. I also compile the questions after reading the docs and ask them in 1:1s.

One challenge I ran into is digging into the rabbit hole of docs. Each document leads to more documents with numerous unfamiliar metrics, acronym names, and projects. This is especially challenging as a manager—if each of your team members has three projects, then five people means fifteen projects to catch up on. At one point, my browser’s “To Read” tab group had over 30 tabs open.

Luckily, AI tools are here to rescue. While reading all the docs one by one is helpful for a detailed understanding, AI tools are great for providing a holistic view and connecting the dots. For example:

• Glean (DoorDash) has access to internal docs and Slack. I often chat with Glean, asking questions like “How is GOV calculated?” or “Provide a summary of project X, including the goal, timeline, findings, and conclusion.” It links to the source documents, so I can dive deeper quickly if needed.

• NotebookLM – I shared a set of docs on a specific topic with it and asked it to generate summaries and mind maps. This helped me organise my thoughts. It can also create podcasts, which are sometimes more digestible than reading long documents.

• ChatGPT (or similar) can be connected to internal knowledge bases to serve a similar purpose.

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III — Build Data Knowledge

Building data knowledge is as important as building domain knowledge for data scientists. As a front‑line manager, I hold myself to a simple standard: I should be able to do hands‑on data work well enough to provide practical, credible guidance to my team.

Here is what helped me ramp up quickly:

• Set up the tech stack in week one.
  I recommend configuring the developer environment early. Access issues, permissions, and quirky environment problems always take longer than expected. The earlier you have everything set up, the sooner you can start playing with the data.

• Make full use of AI‑assisted data tools.
  Every tech company is integrating AI into its data workflows. For example, at DoorDash we have Cursor connected to Snowflake with internal data knowledge and context. It can generate SQL queries and analyses grounded in our data. Although the generated queries aren’t 100 % accurate, the suggested tables, joins, and past queries it points to serve as excellent starting points. It won’t replace your technical judgment, but it dramatically reduces the time to first insight.

• Explore the data directly.
  Pull a few key tables, run simple exploratory queries, and visualise the results. Ask yourself:

  1. What are the primary keys and foreign keys?
  2. Which columns contain missing or anomalous values?
  3. What are the most common metrics and how are they calculated?

• Document what you learn.
  Keep a living markdown file or notebook with:

  • Data dictionary entries you discover
  • Common transformation pipelines
  • Known data quality issues and their mitigations

  Sharing this document with your team early on demonstrates initiative and creates a reference for future newcomers.

Checklist for a Data‑Science Onboarding

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IV — Start Small and Contribute Early

While onboarding is primarily about learning, I strongly recommend starting small and contributing early. Early contributions signal ownership and build trust — often faster than waiting for a “perfect” project. Here are some concrete ways:

• Improve the onboarding documentation
  As you go through the onboarding docs, you’ll encounter random technical issues, broken links, or outdated instructions. Fixing them yourself is valuable, but enhancing the documentation shows you’re a team player and want to make onboarding better for future hires.

• Build documentation
  No company has perfect documentation — from my own experience and conversations with peers, most data teams struggle with outdated or missing docs. While you’re onboarding and not yet busy with large projects, it’s the perfect time to fill those gaps.

  Examples:

  • Created a project directory for my team to centralize past and ongoing projects, including key findings and points of contact.
  • Compiled a collection of metric heuristics, summarizing causal relationships between metrics learned from past experiments and analyses.

  These documents also become valuable context for AI agents, improving the quality and relevance of AI‑generated outputs.

• Suggest process improvements
  Every data team operates differently, with its own pros and cons. Joining a new team gives you a fresh perspective on processes and may reveal opportunities to improve efficiency. Thoughtful suggestions based on your past experience are highly valuable.

In my opinion, a successful onboarding aims to establish cross‑functional alignment, business fluency, and data intuition.

Onboarding Checklist

Week 1–2: Foundations

• Meet key business partners
• Get added to core cross‑functional meetings
• Understand team focus and high‑level priorities
• Set up tech stack, access, and permissions
• Write your first line of code
• Read documentation and ask questions

Week 2–6: Get Your Hands Dirty

• Deep‑dive into team OKRs and commonly used data tables
• Deep‑dive into your focus area (more docs and questions)
• Complete a starter project end‑to‑end
• Make early contributions: update outdated info, build a piece of documentation, or suggest a process improvement

Week 6–12: Ownership

• Speak up in cross‑functional meetings and provide data‑informed viewpoints
• Build trust as the “go‑to” person for your domain

Onboarding looks different across companies, roles, and seniority levels, but the principles stay consistent. If you’re starting a new role soon, I hope this checklist helps you ramp up with more clarity and confidence.