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Lead Data Scientist - Personalization & Recommendation Engines

Lead Data Scientist - Personalization & Recommendation Engines

HarnhamSan Jose, CA, US
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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 TensorFlow.
  • 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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