Sr. Data Scientist

Senior 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.

#DICEJOBS

#RT

snowflake, AWS sagemaker, data scientist, python, ml ops