Senior Level Science Advisor Jobs
Senior level science advisor jobs place experienced professionals in charge of shaping research direction, owning advisory outcomes, and leading the cross-functional teams that deliver on complex scientific challenges. Roles are concentrated in Education, Biotechnology & Pharmaceuticals, and Technology & Software, with a mix of on-site, remote, and hybrid settings, and employers like Capital One, Revolution Medicines, and Revolution Medicines hiring at this level now.
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Showing 5 of 134+ Senior Level Science Advisor jobs
Position Profile
The Senior Data & AI Engineer will need to have deep hands‑on experience in Snowflake, Microsoft Fabric (incl. OneLake), and healthcare data ecosystems. The ideal candidate understands data modeling, data integration, and data transformation across structured and unstructured sources, and can build machine learning pipelines that operate on claims and clinical data. You’ll design secure, scalable data platforms; map and normalize data across payers, providers, CMS datasets, EHR systems, and HIEs; and operationalize AI tools to drive measurable outcomes in cost, quality, and member/patient experience.
Responsibilities
Data Platform Engineering:
- Architect, implement, and optimize data solutions in Snowflake and Microsoft Fabric (incl. OneLake, Lakehouses, Warehouses, and Data Engineering pipelines).
- Build robust ingestion frameworks for batch and streaming data (e.g., ADLS, EventHub, APIs, SFTP) with lineage and governance.
- Manage data security, privacy, and compliance (HIPAA/PHI; role‑based access, masking, tokenization, de‑identification).
Data Modeling & Integration:
- Design conceptual/logical/physical models (normalized, dimensional/star, data vault where appropriate).
- Implement data mapping and transformations for structured (claims, eligibility, provider, enrollment) and unstructured (clinical notes, PDFs) data.
- Harmonize healthcare data using FHIR/HL7/C‑CDA, X12/EDI 837/835, NCPDP, and CMS standards; reconcile and link records across EHR and HIE sources.
Analytics & Machine Learning:
- Build ML pipelines for risk stratification, cost/utilization forecasting, fraud/waste/abuse detection, quality measure computation (e.g., HEDIS), and care gap identification.
- Operationalize models with MLOps (experiment tracking, reproducibility, CI/CD, monitoring, drift detection).
- Leverage LLMs/AI tools for data quality, entity resolution, summarization, and clinical insights—ensuring safety, bias checks, and auditability.
Governance & Observability:
- Implement data cataloging, lineage, and metadata (e.g., Microsoft Purview or equivalent).
- Establish quality SLAs, validation rules, profiling, and automated anomaly detection.
- Instrument pipelines for cost, performance, and reliability (e.g., Snowflake resource monitors, Fabric capacities).
Collaboration & Delivery:
- Work with product owners, clinicians, actuaries, and analytics teams to translate requirements into scalable solutions.
- Produce clear documentation, data dictionaries, and mapping specs; mentor engineers and analysts.
- Contribute to architectural roadmaps, reference patterns, and best practices across the enterprise. Maintains and enhances professional skills. Adheres to high standards of personal and professional conduct.
Minimum Qualifications
- 8+ years in data engineering/analytics; 5+ years hands‑on with Snowflake (compute, storage, virtual warehouses, tasks, streams, Snowpipe, Time Travel, RBAC, row/column masking, data sharing, Dynamic Tables).
- 2+ years with Microsoft Fabric (including OneLake, Lakehouses, Warehouses, Dataflows Gen2, Notebooks, Pipelines; capacity management).
- Strong data modeling expertise (dimensional/star, 3NF, data vault; surrogate keys, SCD types, conformed dimensions).
- Data integration & transformation proficiency: SQL (advanced), dbt or Fabric Dataflows/Power Query M, ADF/Synapse/Fabric Pipelines, Python for ETL/ELT.
- Mapping from CMS data (e.g., Medicare datasets, claims/encounters), X12/EDI, FHIR/HL7, provider and eligibility.
- Experience with structured (tables, CSV, Parquet) and unstructured (clinical notes, PDFs, blobs) data; NLP pipelines (optional but valued).
- Machine learning: feature engineering, model training/evaluation, and deployment (e.g., scikit‑learn, PyTorch/TensorFlow, Fabric ML/Notebook, Azure ML); production monitoring.
- Security & compliance: HIPAA, PHI handling, auditing, data residency, BAAs; practical access control in Snowflake/Fabric.
- Strong communication; ability to author mapping specs, lineage docs, and present trade‑offs to technical and non‑technical stakeholders.
Preferred Qualifications
- Interoperability: FHIR R4, HL7 v2, X12/EDI (837/835), NCPDP; experience with HIEs and EHR integrations (Epic, Cerner, etc.).
- CMS & payer/provider data: Medicare fee‑for‑service, MA, Medicaid, CCW, APCD, and quality programs; risk adjustment (HCC), HEDIS measures.
- MLOps & DevOps: MLflow, DVC, GitHub Actions/Azure DevOps, containerization (Docker), orchestration (Airflow, Fabric Pipelines, or ADF).
- Governance: Microsoft Purview (catalog, lineage, classifications), data quality tools.
- Visualization: Power BI and Fabric Direct Lake; semantic modeling and row‑level security.
- Cloud: Azure (ADLS, Event Hub, Functions, Key Vault, Databricks), optional AWS/GCP exposure.
- Certifications: Snowflake SnowPro Core/Advanced, Microsoft Certified (Azure Data Engineer Associate, Fabric Analytics Engineer).
Position Profile
The Senior Data & AI Engineer will need to have deep hands‑on experience in Snowflake, Microsoft Fabric (incl. OneLake), and healthcare data ecosystems. The ideal candidate understands data modeling, data integration, and data transformation across structured and unstructured sources, and can build machine learning pipelines that operate on claims and clinical data. You’ll design secure, scalable data platforms; map and normalize data across payers, providers, CMS datasets, EHR systems, and HIEs; and operationalize AI tools to drive measurable outcomes in cost, quality, and member/patient experience.
Responsibilities
Data Platform Engineering:
- Architect, implement, and optimize data solutions in Snowflake and Microsoft Fabric (incl. OneLake, Lakehouses, Warehouses, and Data Engineering pipelines).
- Build robust ingestion frameworks for batch and streaming data (e.g., ADLS, EventHub, APIs, SFTP) with lineage and governance.
- Manage data security, privacy, and compliance (HIPAA/PHI; role‑based access, masking, tokenization, de‑identification).
Data Modeling & Integration:
- Design conceptual/logical/physical models (normalized, dimensional/star, data vault where appropriate).
- Implement data mapping and transformations for structured (claims, eligibility, provider, enrollment) and unstructured (clinical notes, PDFs) data.
- Harmonize healthcare data using FHIR/HL7/C‑CDA, X12/EDI 837/835, NCPDP, and CMS standards; reconcile and link records across EHR and HIE sources.
Analytics & Machine Learning:
- Build ML pipelines for risk stratification, cost/utilization forecasting, fraud/waste/abuse detection, quality measure computation (e.g., HEDIS), and care gap identification.
- Operationalize models with MLOps (experiment tracking, reproducibility, CI/CD, monitoring, drift detection).
- Leverage LLMs/AI tools for data quality, entity resolution, summarization, and clinical insights—ensuring safety, bias checks, and auditability.
Governance & Observability:
- Implement data cataloging, lineage, and metadata (e.g., Microsoft Purview or equivalent).
- Establish quality SLAs, validation rules, profiling, and automated anomaly detection.
- Instrument pipelines for cost, performance, and reliability (e.g., Snowflake resource monitors, Fabric capacities).
Collaboration & Delivery:
- Work with product owners, clinicians, actuaries, and analytics teams to translate requirements into scalable solutions.
- Produce clear documentation, data dictionaries, and mapping specs; mentor engineers and analysts.
- Contribute to architectural roadmaps, reference patterns, and best practices across the enterprise. Maintains and enhances professional skills. Adheres to high standards of personal and professional conduct.
Minimum Qualifications
- 8+ years in data engineering/analytics; 5+ years hands‑on with Snowflake (compute, storage, virtual warehouses, tasks, streams, Snowpipe, Time Travel, RBAC, row/column masking, data sharing, Dynamic Tables).
- 2+ years with Microsoft Fabric (including OneLake, Lakehouses, Warehouses, Dataflows Gen2, Notebooks, Pipelines; capacity management).
- Strong data modeling expertise (dimensional/star, 3NF, data vault; surrogate keys, SCD types, conformed dimensions).
- Data integration & transformation proficiency: SQL (advanced), dbt or Fabric Dataflows/Power Query M, ADF/Synapse/Fabric Pipelines, Python for ETL/ELT.
- Mapping from CMS data (e.g., Medicare datasets, claims/encounters), X12/EDI, FHIR/HL7, provider and eligibility.
- Experience with structured (tables, CSV, Parquet) and unstructured (clinical notes, PDFs, blobs) data; NLP pipelines (optional but valued).
- Machine learning: feature engineering, model training/evaluation, and deployment (e.g., scikit‑learn, PyTorch/TensorFlow, Fabric ML/Notebook, Azure ML); production monitoring.
- Security & compliance: HIPAA, PHI handling, auditing, data residency, BAAs; practical access control in Snowflake/Fabric.
- Strong communication; ability to author mapping specs, lineage docs, and present trade‑offs to technical and non‑technical stakeholders.
Preferred Qualifications
- Interoperability: FHIR R4, HL7 v2, X12/EDI (837/835), NCPDP; experience with HIEs and EHR integrations (Epic, Cerner, etc.).
- CMS & payer/provider data: Medicare fee‑for‑service, MA, Medicaid, CCW, APCD, and quality programs; risk adjustment (HCC), HEDIS measures.
- MLOps & DevOps: MLflow, DVC, GitHub Actions/Azure DevOps, containerization (Docker), orchestration (Airflow, Fabric Pipelines, or ADF).
- Governance: Microsoft Purview (catalog, lineage, classifications), data quality tools.
- Visualization: Power BI and Fabric Direct Lake; semantic modeling and row‑level security.
- Cloud: Azure (ADLS, Event Hub, Functions, Key Vault, Databricks), optional AWS/GCP exposure.
- Certifications: Snowflake SnowPro Core/Advanced, Microsoft Certified (Azure Data Engineer Associate, Fabric Analytics Engineer).
See All 134+ Senior Level Science Advisor Jobs
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Find JobsSenior Level Science Advisor Job Market
Who's Hiring
- Revolution Medicines16
- Capital One11
- HDR4
- Lyft4
Top Industries Hiring
- Education24
- Biotechnology & Pharmaceuticals22
- Technology & Software18
- Banking & Financial Services9
- Consulting & Professional Services9
Senior Level Science Advisor Jobs: Frequently Asked Questions
How do I get a senior level science advisor job?
Employers at this level look for demonstrated ownership of high-stakes scientific decisions, not just technical competence. Candidates who stand out can point to specific projects they directed from question to recommendation, show experience advising senior stakeholders or policy teams, and have a record of mentoring junior scientists. Tailoring your application to show strategic impact, not just execution, is what moves you past the screening stage.
Which companies hire senior level science advisors?
Companies hiring senior level science advisors right now include Capital One, Revolution Medicines, and Revolution Medicines, based on current listings on Migrate Mate as of November 2026. At this level, hiring tends to come from large research-intensive organizations, federal agencies and contractors, life sciences firms, and consulting groups that advise on science policy or regulatory strategy.
Are there remote senior level science advisor jobs?
Yes, though availability varies by sector and employer. About 43% of senior level science advisor openings are remote or hybrid as of November 2026, with fully on-site roles more common in government, lab-based research, and positions requiring access to classified or regulated facilities. Advisory and policy-focused roles tend to offer the most location flexibility at this level.
What makes a science advisor role senior level?
Senior level science advisor roles are defined by scope and ownership. Rather than supporting a team's analysis, you set the scientific direction and are accountable for the quality and credibility of the advisory output. These roles typically involve leading teams or working groups, engaging directly with executive or government stakeholders, and mentoring less experienced scientists. Deep subject matter expertise combined with the ability to translate complex findings into decisions is the core expectation.
Which industries hire the most senior level science advisors?
Senior Level science advisor roles concentrate in Education, Biotechnology & Pharmaceuticals, and Technology & Software, based on current listings on Migrate Mate as of November 2026. These sectors drive demand because they face complex regulatory environments, evolving scientific evidence, or high-stakes policy decisions that require credible expert guidance at a leadership level rather than general research support.