Job DescriptionSenior Data Scientist About the Role 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.
Sr. Data Scientist Primary Responsibilit - 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.
Sr. Data Scientist Required Data Science & Engineering Qualifications and Experience - 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.
Sr. Data Scientist Preferred Pharmaceutical / Regulated-Industry Experience Qualifications - 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.
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Job Requirementssnowflake, AWS sagemaker, data scientist, python, ml ops
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