Position Overview
Raya is a technology company focused on providing utility through its apps, Raya and Places – tools for discovering exciting people and places globally. We are a membership-based community built around core values of trust, curation, and authenticity. Our diverse team comes from companies like Apple, Google, Amazon, Meta, Snap, Twitter, Instacart, and PayPal. In this role, you will be the primary data scientist on our Places app, owning the data strategy that builds and optimizes a recommendations-first experience. You’ll dig into ambiguous questions, build dashboards, collaborate with ML, engineering, and design teams, and have a visible impact across our entire product.
Key Responsibilities
- Build and maintain dashboards and metrics to help the team self-serve and monitor key performance indicators.
- Dig into user behavior, search logs, and engagement data to uncover patterns and generate new hypotheses.
- Help improve data organization and infrastructure, reducing ad hoc work and improving scalability.
- Design and analyze A/B tests and other experiments to guide product decisions and measure impact.
- Leverage off-the-shelf models and experimentation frameworks to optimize relevance and personalization features.
- Apply and evaluate large language models (LLMs) for tasks such as semantic search, personalization, and user understanding, while developing expertise in LLM evaluation methodologies and best practices.
- Collaborate with ML engineers and backend/infra teams to make incremental improvements to existing recommendation systems and embedding pipelines.
- Contribute to a data-driven culture by clearly communicating findings and empowering stakeholders with reliable, accessible insights.
Required Qualifications
- 3+ years of experience in data science or analytics, ideally with exposure to product-focused teams. Experience with recommendation and/or search ranking systems is a big plus.
- Strong Python and SQL knowledge; ability to write clean, reproducible code and work with large-scale datasets.
- Experience with modern data tools, including dbt and Snowflake/Databricks for data modeling and transformation; Mixpanel or Segment for product analytics and event tracking; and Looker, Omni, or Tableau for visualization and dashboarding.
- Familiarity with common ML libraries (e.g., Scikit-learn, XGBoost); knowledge of deep learning frameworks like PyTorch is a bonus.
- Experience applying and evaluating LLMs in practical, product-oriented settings (e.g., building or optimizing search, recommendations, or personalization tools).
- A strong analytical mindset with the ability to break down complex problems, design experiments, and generate actionable insights.
- Comfortable collaborating cross-functionally with product, engineering, and design teams.
Benefits & Perks
- Comprehensive medical and dental coverage
- 12 weeks paid parental leave
- $1,000 a year travel stipend
- Great culture and learning opportunities