About the role
Responsibilities:
- Conduct deep analysis of commerce data to derive insights and identify gaps and new opportunities.
- Develop scalable and effective machine-learning models and optimization strategies to solve business problems across advertising, pricing, and listings.
- Define and lead science initiatives from problem framing through production deployment in a high-ambiguity environment.
- Identify and build the sequential feedback loops that make decisions improve over time.
- Design evaluation frameworks to measure the quality and business impact at scale.
- Work closely with engineering, analytics, and product teams to take models from experimentation into production.
Requirements:
- 4+ years in applied ML/AI and data science. A master's or PhD in a quantitative field is a plus.
- Deep proficiency in Python, SQL, statistics, and data analysis.
- Hands-on experience developing, deploying, and maintaining the end-to-end lifecycle of machine learning models.
- Experience with LLMs, fine-tuning, AI agents, optimization, or sequential decision systems is a strong plus.
- Exposure to e-commerce, marketplaces, advertising, or pricing data is valuable but not essential.
- Strong problem-solving and communication skills; ML research experience is a plus.