Job Description
Senior Data Scientist – Healthcare Data & Advanced Analytics
Location: Remote
Employment Type: Direct Hire
Base Salary: $165,000–$185,000
Bonus: 12.5% Annual Target Bonus
Position Overview
We are seeking a highly technical, hands-on Senior Data Scientist to develop advanced analytics and machine learning solutions using large-scale healthcare data.
This is an end-to-end Data Science position with a significant emphasis on the work that happens before modeling begins. Approximately 60–70% of the role will involve hands-on work with data, including sourcing, combining, cleaning, transforming, validating, and understanding complex datasets.
The ideal candidate will have deep experience working with large, messy healthcare datasets and will be comfortable owning the entire Data Science lifecycle: starting with a business question or hypothesis, sourcing and exploring the underlying data, assessing data quality, preparing datasets, selecting appropriate modeling techniques, developing and validating models, and partnering with engineering teams to move solutions toward production.
We are not looking for someone whose experience is primarily focused on dashboards, reporting, or working with already-curated datasets. The strongest candidate will understand that effective Data Science begins with understanding the underlying data and its limitations.
This role will also leverage Machine Learning, GenAI, and AI-assisted development tools to accelerate solution design, implementation, testing, and documentation while maintaining appropriate human review, validation, security, and traceability.
Key Responsibilities
- Work hands-on with large-scale healthcare datasets throughout the complete Data Science lifecycle.
- Source and combine data from multiple systems to answer complex business questions.
- Build or contribute to data pipelines rather than relying solely on existing dashboards or curated datasets.
- Perform substantial data cleaning, transformation, normalization, and preparation.
- Explore datasets to identify missing data, inconsistencies, quality issues, patterns, and limitations.
- Develop multi-step approaches to assess and improve data quality.
- Clearly communicate data limitations and quality considerations before beginning modeling activities.
- Use Python and SQL to explore, manipulate, analyze, and prepare large datasets.
- Apply statistical analysis, data mining, Machine Learning, and advanced analytics techniques to complex healthcare problems.
- Design, build, test, and validate predictive models.
- Select appropriate modeling methodologies based on the specific business problem and explain the reasoning behind those decisions.
- Evaluate model performance, robustness, and business relevance.
- Partner with Data Engineers to harden analytical solutions and move them into operational production environments.
- Apply appropriate MLOps practices throughout model development and productionization.
- Use AI and GenAI tools to accelerate requirements clarification, solution design, coding, querying, notebooks, pipelines, testing, and documentation.
- Apply human review and validation to AI-generated outputs for correctness, security, performance, and maintainability.
- Use techniques such as prompt engineering, grounding, evaluation frameworks, and benchmarking to improve AI-assisted solutions.
- Define acceptance criteria, testing strategies, data validation methods, and model QA approaches.
- Create meaningful visualizations and communicate complex analytical findings to technical and non-technical audiences.
- Translate technical findings and modeling decisions into insights tied directly to business needs.
- Partner with Product, Data Engineering, Data Architecture, business leaders, and other stakeholders to develop new data-driven products.
- Support commercialization efforts and the development of new revenue-generating data products.
- Mentor Data Scientists, Analysts, Engineers, and other team members.
- Develop training and educational materials to improve adoption of advanced analytics, AI, and Data Science capabilities.
- Maintain strong awareness of healthcare data privacy, security, governance, and appropriate data usage.
Required Qualifications
- 5+ years of professional experience in Data Science, Machine Learning, advanced analytics, or closely related technical data roles.
- Significant hands-on experience working with healthcare data.
- Experience with healthcare datasets such as:
- Pharmacy / pharmaceutical data
- Claims data
- PBM data
- Prior authorization
- Medicare or payer data
- Patient journey data
- Healthcare network data
- Strong experience working with large, messy, complex datasets.
- Proven ability to source, combine, clean, structure, transform, and validate raw data.
- Strong SQL skills.
- Strong Python experience ; exceptional candidates with deep R and SQL expertise may also be considered.
- Experience performing data exploration and identifying data quality deficiencies before modeling.
- Strong predictive modeling and data mining experience.
- Hands-on Machine Learning experience.
- Ability to select appropriate modeling techniques based on the business problem and clearly explain why a particular methodology was selected.
- Deep understanding of statistical and modeling methodologies rather than superficial familiarity with Data Science terminology.
- Experience across the full Data Science lifecycle, including:
- Business problem / hypothesis development
- Data sourcing
- Data exploration
- Data preparation and transformation
- Data quality assessment
- Model selection and development
- Testing and validation
- QA / review
- Productionization
- Ongoing improvement
- Experience partnering with Data Engineers to move analytical work toward production.
- Experience using AI / GenAI assistants as part of technical workflows.
- Experience validating AI-generated code, analysis, or technical outputs.
- Strong analytical curiosity and ability to ask thoughtful exploratory questions before jumping into a solution.
- Ability to solve ambiguous, complex problems with limited supervision.
- Strong communication skills with the ability to explain technical decisions and analytical findings to non-technical audiences.
- Knowledge of healthcare data privacy and security requirements, including HIPAA.
- Ability to speak in depth about technical accomplishments and explain the reasoning behind previous analytical and modeling decisions.
Qualifications
- Master's degree or PhD in Data Science, Statistics, Mathematics, Computer Science, Public Health, or another quantitative discipline.
- Prior authorization experience.
- Pharmacy, PBM, claims, payer, or broader healthcare ecosystem experience.
- Experience working with healthcare transactional data at significant scale.
- Google Cloud Platform (GCP) experience.
- Experience with MLOps frameworks and environments such as GCP, Databricks, or Snowflake.
- Experience with TensorFlow, PyTorch, or similar Machine Learning frameworks.
- Advanced experience with Large Models or Natural Processing.
- Experience with retrieval-augmented (RAG), LLM evaluation, fine-tuning, or AI orchestration.
- Experience with AI-assisted development tools such as Claude Code, GitHub Copilot, Microsoft Copilot, or similar technologies.
- Familiarity with responsible AI practices within regulated environments.
- Experience mentoring technical data professionals.
- Experience developing and delivering technical training.
- Experience supporting data commercialization or development of data-driven products.
What We're Looking For
The strongest candidate will be a true end-to-end Data Scientist who is comfortable getting their hands dirty with the data.
We are not looking for someone whose Data Scientist title primarily represents dashboard development, reporting, or traditional Data Analyst responsibilities.
You should be able to take a complex business question and independently work through the process of determining what data is needed, where that data comes from, how different sources need to be combined, what deficiencies exist within the data, how it needs to be cleaned and transformed, and whether it is appropriate for modeling.
From there, you should be able to select an appropriate statistical or Machine Learning approach, explain why you selected it, validate the results, connect those results to the original business problem, and partner with engineering teams to move the solution toward production.
Deep healthcare data experience is particularly important. Experience across pharmacy, claims, PBM, prior authorization, payer, Medicare, or patient journey data is especially relevant. Candidates whose healthcare experience is primarily limited to EHR reporting may not have the breadth of healthcare data experience needed for this position.
This team will ask detailed technical questions about previous work, so candidates should be comfortable explaining exactly what they personally did, how they worked with the underlying data, why they selected specific methodologies, what challenges they encountered, how they validated their work, and how the solution ultimately supported the business.
This position is fully remote. Candidates cannot be located in California, New York, or Vermont. Candidates must be a U.S. or Green Card holder. Sponsorship is not available for this position.
On-Demand Group (ODG) provides employee benefits which includes healthcare, dental, and vision insurance. ODG is an equal opportunity employer that does not discriminate on the basis of , , , , , , , , or any other characteristic protected by law.