Revolutionize your charging experience with our Contactless Wireless Charging Stations – the market. Backed by Techstars.
About the role
As a Senior Data Scientist for personalization, you will own the science behind the recommendation engine that powers each user’s personalized product feed. Starting from our user-saved product signals and a live catalog ingested from thousands of retailer feeds, you will design, build, evaluate, and continuously improve the models that learn each user’s taste across brand, category, color, price point, and fit.
What they're looking for
- You combine the modeling depth of an applied/data scientist with the pragmatism to ship end-to-end. You bring:
- Strong data science fundamentals: statistics, experimental design, and evaluation methodology, with the analytical ability to turn model results into clear product and business decisions
- Demonstrated ownership of the full A/B testing lifecycle: designing experiments, running them, reading them out, and deciding, not just reporting offline metrics
- Experience designing, training, and deploying embedding models and vector retrieval (e.g., Milvus, Pinecone, or Vertex AI Vector Search) for product or content similarity at catalog scale
- Direct experience with cold-start / sparse-signal personalization: building useful recommendations from a new catalog, new users, or both. This is a core, day-one challenge of the role
- Strong Python and modern ML frameworks (PyTorch, TensorFlow, or JAX) plus the standard scientific stack (pandas, NumPy, scikit-learn). You write production-quality code, not just notebooks
More about this role
Job Title
Senior Data Scientist
Job Description
About The Position |
As a Senior Data Scientist for personalization, you will own the science behind the recommendation engine that powers each user’s personalized product feed. Starting from our user-saved product signals and a live catalog ingested from thousands of retailer feeds, you will design, build, evaluate, and continuously improve the models that learn each user’s taste across brand, category, color, price point, and fit.
This is a hands-on, full-cycle role. You will take a recommendation problem from raw data all the way to a production model running on our existing MLOps stack — you own the model layer, not the infrastructure. Our platform team already operates the feature store, serving, and autoscaling; your job is to decide what to model, prove it works through rigorous offline and online experimentation, ship it, and iterate as behavioral signals accumulate.
A defining challenge of this role is cold-start. We are launching with a small behavioral dataset and a catalog scaling from hundreds of thousands of products toward tens of millions. You will need strong commerce and product-data intuition to produce...
Browse similar: AI jobs · AI startup jobs · Startup jobs