Senior Data Scientist, Recommendation System

Position:
Senior Data Scientist, Recommendation Systems

This position open for Principal level too, depend on depth of experience, leadership in methodological innovation and impact in production systems.

Location:

Open globally (strong overlap with GMT+3 to GMT+8 preferred)

We're looking for a Senior Data Scientist, Recommendation Systems to own and orchestrate our recommendation infrastructure across all surfaces. You'll design how multiple recommendation models work together, balance competing constraints (user preferences, pharma content, content freshness, diversity), and build the semantic search and embedding layer that powers personalized experiences for doctors.

This isn't just about building another recommendation algorithm, it's about architecting how collaborative filtering, user-based filtering, embeddings, and business constraints combine into a unified system that serves the right content to the right doctor at the right time. You'll work at the intersection of recommendation algorithms, constraint optimization, vector databases, and production systems.

You'll be the person who ensures our recommendation strategy is coherent, scalable, and balances user value with business objectives.

• Design overall recommendation architecture across all surfaces (Feed, Search, Swipe, Network Recommendation)
• Orchestrate how multiple models work together built by other DS.
• Build multi-objective optimization frameworks that balance user preferences, business constraints and rules, content freshness, diversity and other attributes required.
• Design ranking and retrieval strategies
• Define recommendation quality metrics (relevance, diversity, novelty, coverage)
• Work with other DS to A/B test recommendation strategies

• Measure quality metrics: click-through rate, engagement, diversity, coverage, serendipity
• Identify failure modes: why do recommendations fail? What patterns are we missing?
• Apply statistical methods to understand recommendation effectiveness
• Debug recommendation issues (filter bubbles, cold start, popularity bias)
• Work with Behavioral DS to incorporate behavioral signals into ranking
• Collaborate with existing DS on model improvements

• Build and maintain vector database schema for semantic search and retrieval. You will work with Data Architect, Data Engineer and Dev Ops to product ionize your work and infrastructure stuff.
• Create and optimize embedding strategies for: medical content, doctor profiles and constraint’s campaigns
• Implement retrieval systems
• Fine-tune embedding models for medical domain (or work with NLP DS for custom models)
• Optimize for latency and scale (serving recommendations to thousands of doctors)
• Design embedding versioning and retraining pipelines

• With Junior DS
  :
  Provide technical direction on how their models integrate into the overall system
• With Behavioral Science DS
  :
  Incorporate behavioral features into recommendation ranking
• With Experimentation DS
  :
  Design A/B tests for recommendation strategies
• With NLP DS
  :
  Collaborate on embedding quality for medical content
• With Data Engineers & Data Architects
  :
  Design vector database infrastructure and embedding pipelines

We welcome candidates with deep expertise in architecting and orchestrating production recommendation systems  are a technical leader who thinks in terms of systems, trade-offs, and infrastructure, not just algorithms.

Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, Physics, or relevant field

Master's or PhD

Min. 6 years in recommendation systems, ML engineering, or related fields

Deep understanding of recommendation algorithms, experience with embeddings and vector databases, ability to architect systems that balance multiple objectives, and production ML experience

• Recommender System Architecture & Orchestration:
• Designing hybrid recommendation systems that combine collaborative filtering, content-based, and embedding-based approaches…