# Senior Data Scientist at Meredot

- Company: Meredot
- What the company does: Revolutionize your charging experience with our Contactless Wireless Charging Stations – the market. Backed by Techstars.
- Company website: https://meredot.com
- Type: Startups (AI role)
- Level: Senior
- Location: 2 Locations
- Work setup: On-site
- Pay: $175K to $190K base salary per year (USD)
- Posted: 2026-08-25
- Apply by: 2026-10-09
- Apply: https://meredith.wd5.myworkdayjobs.com/en-US/EXT/job/New-York--NY-225-Liberty/JD---Senior-Software-Engineer-1--ML_JR15437
- Page: https://www.1752.vc/careers/jobs/meredot-senior-data-scientist/

## 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