onsite
Associate Director, Data Science - Digital Endpoints
Data Scientist
Lead a data science team developing cloud‑native pipelines and deep‑learning models for digital endpoint analysis in pharma R&D, leveraging Python, Spark, and AWS to process distributed data at scale.
About the role
Key Responsibilities
- Architect and oversee end‑to‑end cloud data pipelines that ingest, transform, and store high‑volume digital endpoint data.
- Design, train, and deploy deep‑learning models for signal detection, biomarker discovery, and predictive analytics.
- Collaborate with R&D scientists to translate experimental data into actionable insights and automated workflows.
- Guide a multidisciplinary team of data scientists, engineers, and analysts, fostering best practices in code quality, reproducibility, and model governance.
- Evaluate and integrate emerging technologies (e.g., container orchestration, serverless compute) to improve scalability and cost efficiency.
Requirements
- Advanced degree (PhD or MS) in Computer Science, Bioinformatics, Statistics, or related field with 8+ years of experience in data science and machine learning.
- Proficiency in Python and deep‑learning frameworks such as TensorFlow or PyTorch.
- Strong background in building and operating data pipelines on AWS (e.g., S3, EMR, Lambda) using Apache Spark and SQL.
- Hands‑on experience with containerization (Docker) and orchestration tools for reproducible model deployment.
- Demonstrated ability to lead technical teams and deliver production‑grade analytics solutions in a regulated pharma environment.
Skills
pythontensorflowawsapache sparksqldocker