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Lead Data Scientist - Personalization & Recommendation Engines San Francisco, CA
Job in
San Francisco, San Francisco County, California, 94199, USA
Listed on 2026-02-16
Listing for:
Harnham
Part Time
position Listed on 2026-02-16
Job specializations:
-
IT/Tech
AI Engineer, Machine Learning/ ML Engineer
Job Description & How to Apply Below
Job Description
Lead Data Scientist - Personalization & Recommendation Engines
Location: SF Bay Area - Hybrid (3 days/week onsite)
Salary: $200-260k base + Equity
A leading commerce marketplace with 130M+ users and billions of daily events is hiring a Staff Machine Learning Scientist to drive innovation across personalization, feed ranking, computer vision, and GenAI. You'll work on high-impact ML solutions that directly shape user experience and business outcomes at massive scale.
What You’ll Do
- Lead full-lifecycle ML projects from idea to production across core areas like personalization, trust & safety, marketing optimization, and user engagement.
- Own the ML development process—from data exploration and feature engineering to model training, deployment, and post-launch optimization.
- Collaborate cross-functionally with ML engineers, PMs, and business stakeholders to identify and prioritize high-leverage initiatives.
- Experiment with emerging AI techniques, including GenAI, Computer Vision, and LLMs, to push the boundaries of what’s possible on the platform.
- Build scalable, production-ready ML systems that enhance key metrics like retention, engagement, and conversion.
What You Bring
- 7–10 years of experience building, deploying, and maintaining ML models at scale.
- Deep expertise in Python, SQL, Spark (PySpark or Scala) and frameworks like PyTorch or Tensor Flow.
- Proven track record in consumer tech or large-scale marketplaces companies.
- Hands-on experience with CNNs, Transformers, Vision Transformers, and personalization algorithms.
- Background in user behavior modeling, search relevance, or real-time data systems.
- Strong foundation in experimentation (A/B testing), statistics, and applied ML.
- Exceptional communication skills and the ability to translate technical insights into business value.
- Experience with LLMs, RAG (Retrieval-Augmented Generation), or PEFT (Parameter-Efficient Fine-Tuning) techniques.
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