Sr. Data Scientist

Our client is looking for a Senior Data Scientist for a long-term engagement with a pharmaceutical client. This is a hands-on, end-to-end role embedded directly within the client's data and AI team. The data scientist builds machine learning models and predictive analytics solutions and owns the data engineering behind them-including transformation, cleanup, and automated ingestion of new data-as well as the full MLOps lifecycle.

The client is establishing its production ML capability and does not yet have a model running in production, so this person should be comfortable taking raw, messy data all the way to a monitored production model and helping shape the operating model and MLOps framework as they go. This individual operates independently as a trusted member of the team while bringing the collective experience, methodologies, and reusable frameworks of our broader data and AI practice to bear., * Build, train, and validate ML models supporting predictive analytics use cases (e.g., batch/resource optimization and exception pattern recognition in a manufacturing setting).

• Develop and maintain data transformation and cleanup pipelines in Snowflake, turning raw source data into modeling-ready datasets.
• Automate ingestion and processing of new and recurring data feeds, accounting for data that may arrive from external systems or partners with latency.
• Own the full ML lifecycle in AWS SageMaker (or Snowflake-native ML where appropriate): training, deployment, monitoring, retraining, and drift detection.
• Implement MLOps practices: CI/CD for ML, model versioning, automated pipelines, and performance monitoring.
• Evaluate the client's existing environment-including Snowflake's native MLOps capabilities-and recommend the most cost-effective, fit-for-purpose path before building from scratch.
• Contribute to a blueprint for the client's ML operating model: where models are trained, how they are promoted and deployed, and how users access outputs (e.g., dashboards or batch inputs).
• Collaborate with stakeholders to translate business requirements into production solutions, and deliver work phase by phase, starting with the lowest-effort, highest-value use cases.

• Proven experience building and deploying ML models in production.
• Strong Python and ML libraries (scikit-learn, XGBoost, TensorFlow, or PyTorch).
• Strong data engineering skills: SQL, data transformation, and pipeline development (e.g., dbt, Snowflake-native, or equivalent).
• Hands-on Snowflake experience for warehousing, transformation, and ingestion.
• Hands-on AWS SageMaker experience across the model lifecycle.
• MLOps tooling: CI/CD for ML, monitoring, automated retraining, and version control.
• Ability to work independently and reliably in a staff augmentation capacity as part of the client's team., * Prior experience in pharma, life sciences, or another regulated industry.
• Familiarity with GxP, HIPAA, or 21 CFR Part 11.
• Understanding of data privacy and validation requirements in regulated settings.

Sr. Data Scientist Additional Skills

• Orchestration (Airflow, Step Functions), infrastructure-as-code (Terraform/CloudFormation), and containerization (Docker).

What Sets a Strong Sr. Data Scientist Candidate Apart

Above all, we value honesty about capability. The client is looking for a resource whose demonstrated experience matches their resume-someone they can trust to assess the landscape, make sound technical judgments, and pick up new areas (such as agentic or generative AI) as the engagement matures. A candidate who can build production ML solutions while helping mature the surrounding framework will be an ideal fit.