Dear Netflix Talent Acquisition team,
I want to throw my hat in for the Data Scientist role you have posted on your careers page. For a good while now my focus has been data science, and I would be glad to lend that to your team.
Before writing I dug into Netflix, and what stayed with me was your work on the recommendation models and the research posts your team keeps putting out on causal inference. It reads like a strong moment to join, and I would gladly aim my data science experience at it.
Reading through the posting, the three areas that count most for you are machine learning and predictive modeling, statistics and experimentation and feature engineering and model deployment. Those separate a strong data scientist from an average one, and I have hard results in each.
On machine learning and predictive modeling, I work with Python, scikit-learn and PyTorch. As a Data Scientist at Meta, I built a churn model in PyTorch that cut monthly churn by 8 points. Beyond that, I built the reusable feature library the science team now trains on.
For statistics and experimentation, I rely on A/B testing, causal inference and hypothesis testing. In my time as a Data Scientist at Meta, I set up an experimentation framework with real power analysis that caught three false-positive wins before rollout.
On feature engineering and model deployment, I bring feature stores, MLflow and SQL. Working as a Data Scientist at Meta, I shipped a model-serving stack that took a model from notebook to production in a week. Beyond that, I wrote the model-evaluation playbook the whole team follows.
I would be glad to walk you through any of it in an interview and lay out why I fit. I am ready to dig into the data, help the team ship models that hold up, and keep growing with it.
Thank you for reading, and I hope we can set up a time to talk.
Yours sincerely,
Theo Script
theo.script@gmail.com