The company is looking to build and influence a data strategy from the ground up to optimize a recommendations-first product experience, impacting how users discover and connect with content and people.
Requirements
- Strong proficiency in Python and SQL, with experience managing and analyzing large-scale datasets.
- Hands-on experience with modern data tools, such as dbt and Snowflake/Databricks for transformation; Mixpanel, Segment, or equivalent for event tracking; and Looker, Omni, or Tableau for visualization.
- Familiarity with machine learning libraries (e.g., Scikit-learn, XGBoost) and optional exposure to deep learning frameworks like PyTorch.
- Practical experience applying or evaluating LLMs in real-world, product-oriented contexts (e.g., search, recommendations, personalization).
- Strong analytical and experimental design skills, with the ability to translate complex findings into clear product insights.
- Experience working with recommendation systems, search ranking, or personalization algorithms is highly preferred.
Responsibilities
- Build and maintain dashboards and key performance metrics that empower the team to monitor performance and self-serve insights.
- Analyze user behavior, engagement patterns, and search data to uncover opportunities for product optimization.
- Partner with engineering to improve data organization and infrastructure, ensuring scalability and reliability.
- Design, execute, and evaluate A/B tests and other experiments to inform product strategy and measure feature impact.
- Leverage existing ML models and experimentation frameworks to enhance relevance, recommendations, and personalization.
- Apply and assess large language models (LLMs) for tasks like semantic search, personalization, and user understanding, while developing expertise in LLM evaluation best practices.
- Collaborate with ML engineers and infrastructure teams to refine recommendation systems and embedding pipelines.
Other
- 3+ years of experience in data science or analytics, ideally supporting product teams or user-facing applications.
- Effective communicator and collaborator who enjoys partnering with product managers, engineers, and designers.
- This role is ideal for someone who thrives in ambiguity—comfortable exploring open-ended questions, building metrics and dashboards, and working alongside ML and infrastructure teams to make continuous, data-driven improvements.
