Senior Data Scientist

Prominence Advisors

8d•Hybrid

About The Position

Prominence Advisors is actively seeking a Senior Data Scientist to join our team. Prominence is a healthcare and life sciences technology and consulting firm dedicated to helping leading organizations do more with their data. With deep expertise across the full ecosystem — spanning providers, payers, and life sciences — we turn complex data into actionable insight. We deliver a full spectrum of services, including advisory, analytics, optimization, and IT staffing. Our work helps organizations operate more efficiently, improve patient outcomes, and deliver measurable financial impact through smarter use of data. Recognized as a 2x Best in KLAS organization, we partner with more than 120 health systems and 40 life sciences organizations, including 7 of the top 10 U.S. News & World Report-ranked institutions. Through our life sciences brands, Tegra Analytics and WLH Consulting & Learning Solutions, we extend these capabilities to pharmaceutical and biotech organizations — combining data, consulting, and learning to advance commercial excellence. By connecting strategy, data, and execution, we help organizations translate data into clarity, strategy into action, and vision into measurable results. At our core, we are a people-first, values-driven organization that believes strong teams and trusted partnerships are what ultimately make healthcare and life sciences smarter. Prominence is looking for a Senior Data Scientist with deep experience in applied machine learning, statistical modeling, and generative AI to help deliver advanced analytics solutions alongside some of the nation's leading healthcare providers. This role will design, build, and deploy models and AI-powered applications that help healthcare organizations improve patient outcomes, optimize operations, and accelerate research. Helping our customers harness the power of generative AI by building LLM-powered applications that unlock new possibilities for clinical and operational workflows.

Requirements

3–5+ years of professional experience in data science, machine learning, or a closely related quantitative role.
Strong proficiency in Python for data science (pandas, NumPy, scikit-learn) and SQL for data extraction and manipulation.
Hands-on experience building, validating, and deploying machine learning models (classification, regression, clustering, time-series forecasting).
Experience with MLOps best practices (model versioning, CI/CD for ML, experiment tracking with MLflow or similar, continuous monitoring).
Practical experience building AI-powered solutions using LLM APIs, prompt engineering, and RAG pipelines.
Experience with cloud-based data and ML platforms, including at least one of the following: AWS (SageMaker, Redshift, S3), Azure (Azure ML, Synapse, Data Factory), GCP (Vertex AI, BigQuery), Databricks, or Snowflake.
Strong communication skills with the ability to present technical findings to non-technical audiences.

Nice To Haves

Healthcare industry knowledge and experience including EHR data, claims, HL7/FHIR, clinical terminologies.
Experience with population health analytics, risk stratification, or social determinants of health.
Understanding of research enablement workflows including cohort identification, clinical trial matching, and outcomes research.
Experience with vector databases, embeddings, fine-tuning, or LLM evaluation frameworks.
Familiarity with data visualization and BI tools (Tableau, Power BI, or Looker).
Revenue cycle, staffing optimization, or other operational analytics experience.
Advanced degree (Master's or PhD) in a quantitative field such as computer science, statistics, biostatistics, or a related discipline

Responsibilities

Design, build, validate, and deploy machine learning models across the full data science lifecycle, including exploratory analysis, feature engineering, model development, validation, deployment, and monitoring.
Develop advanced analytics solutions for clinical prediction, population health, operational optimization, and research enablement.
Build AI-powered applications leveraging LLM APIs, prompt engineering, and RAG pipelines to support clinical and operational workflows.
Ability to work with diverse healthcare data sources including EHRs, claims, and clinical registries.
Translate complex analytical findings into meaningful, actionable insights for clinical and operational stakeholders.
Manage documentation of best practices and scalable frameworks that can be applied across client engagements.
Mentor and guide client counterparts to build the skills needed to sustain and expand on project deliverables.