What to Expect in your First 90 Days as A Data Scientist
What to Expect in Your First 90 Days as A Data Scientist
Coming from a data analyst without a PhD or technical background
“Welcome to the team! We’re so excited to have you here!”
I couldn’t believe that I had landed my first role as a Data Scientist.
I don’t have a PhD nor do I have a technical background. Instead, I come from a finance background and had worked in FinTech for several years as a risk analyst.
I felt an equal sense of excitement but also imposter syndrome, even after passing rounds of rigorous technical coding interviews.
Coming from an analyst background, I’ve observed that the biggest differences with working in a data science role include:
- Managing timelines of data science projects,
- Communicating with stakeholders on project timelines and implementation.
- Learning the technical aspects such as adopting a test-driven development approach to writing production-level code,
The first 3 months of onboarding as a Data Scientist can make or break your experience.
I’m here to walk you through what to expect in your first 90 days on the job to help ease the nerves around the transition and help you build confidence.
Month #1
In my experience, the first month is about:
- Understanding the organizational structure and team norms,
- Learning about business priorities and knowing where the biggest impact areas are,
- Getting yourself up to speed with the tech stack and systems architecture, and setting up your environment.
In the first month, I focused on:
- Understanding the business: I strived to understand the biggest challenges the business was facing and how my role aligned with the company’s objectives.
- Understanding the data: I reviewed dashboards and familiarized myself with important business metrics and how they’re calculated.
- Familiarizing myself with the company’s data infrastructure: I sought to understand how schemas and tables are organized.
- Meeting the team: I set up introductory meetings with colleagues I would be working closely with to build rapport and understand their needs.
- Discussing expectations with my manager: I worked with my manager to understand and set expectations for my role and work in the first 90 days.
I personally like to do some lightweight work early on, to help navigate my way around the new codebase, build my confidence, and feel more comfortable with committing code.
Some examples of small projects to start with include:
- Adding a chart to an existing dashboard,
- Suggesting and calculating a new metric,
- Adding a simple new feature calculation to the ML repo.
Month #2
The second month is typically where you start to dive into a project.
Scoping out your project
Data Science projects are usually larger in scope and take a longer time to complete than analysis projects. In my opinion, project management skills are often an overlooked yet important area to develop for Data Scientists.
Specifically, I would work with my manager to:
- Define the project scope,
- Estimate project milestones and timelines,
- Gather project requirements and success metrics.
Ironing out the project scope has been crucial to the success of my projects. It’s important to understand:
- Is this just a model refresh with the same features?
- If we’re adding in new features, which feature groups should we explore?
- What quantitative evidence do we have that suggests these feature groups will provide a strong orthogonal signal that isn’t already captured in the current model?
Managing stakeholders
Stakeholders from other teams may enthusiastically suggest feature groups to explore.
It’s important to listen and prioritize them, but also understand that feature engineering is a meaty and time-consuming part of the model development process, so be sure to include them in your project scope.
Since data science projects usually take a month or two to launch, it is important to keep stakeholders updated on the project status. To do so, I like to:
- Update my JIRA tickets regularly with my progress at least once or twice a week,
- Document my process and findings along the way, including what worked and didn’t work,
- Check in regularly early and often with my manager and key stakeholders to align on approach.
Stakeholder management is extremely important for getting buy-in and ensuring you’re on the right track. Bring them along on your journey to keep them engaged and excited about your output!
Month #3
Presenting your work
This is where it all comes together — finalizing and presenting model results to stakeholders.
The purpose of the presentation is to:
- Show model results with clear visualizations,
- Quantify the business impact of the model improvement,
- Get stakeholder buy-in on your work and recommendations with deployment and model usage.
Make sure to address any stakeholder concerns before moving on.
If all goes well, you can move on to the implementation phase after this.
Deploying your model
Every company has their own process, but typically, you would work closely with engineering (data engineers, infrastructure engineers and/or software engineers) on model deployment.
More mature companies tend to a well-defined process around model deployment with good documentation that you can follow.
When putting the model in production, I would make sure to:
- Understand the deployment tools,
- Include integration tests to make sure everything runs end-to-end,
- Put the model into shadow mode first to log scores and ensure score distribution is as expected.
After the model is in production, be sure to set up a monitoring dashboard and alerts for feature and/or model score drifts. Trust me, this will happen sooner or later unfortunately.
Once you’ve verified the model results in shadow mode, report back to the business team and work with the analysts on recommendations of how to use your model. Your work only makes an impact when your model is being used in production!
Congratulations on coming this far! I always like to check in regularly with my manager and gather feedback both from them and peers to build trust.
Summary
The scope and responsibilities of data science roles can vary at different companies. The process I described is typical of a machine learning-focused Data Scientist or ML engineer role, but the general advice is still applicable around:
- Project management,
- Stakeholder management and communication,
- Understanding and working with different tools,
Are you an analyst looking to transition into data science? Are you feeling trapped in your current role?
Are you overwhelmed by the amount of resources out there and don’t know where to start?
Are you feeling a sense of imposter syndrome because you don’t have a PhD or a technical background?
I’ve created a FREE Five-Day Email Course to jump-start your data science career. I transitioned to data science in 2020 without a technical background, and I want to help others do the same. 🚀
What to Expect in your First 90 Days as A Data Scientist was originally published in Towards Data Science on Medium, where people are continuing the conversation by highlighting and responding to this story.
from Datascience in Towards Data Science on Medium https://ift.tt/F6uARUY
via IFTTT
