Note: The job is a remote job and is open to candidates in USA. On-Demand Group is seeking a Senior Data Scientist to develop advanced analytics and machine learning solutions using large-scale healthcare data. The role owns the end-to-end Data Science lifecycle, including sourcing, preparing, validating, and modeling complex datasets, while partnering with engineering and business stakeholders to productionize solutions and develop data-driven products.
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
Skills
- 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
- 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
- Candidates cannot be located in California, New York, or Vermont
- Candidates must be a U.S. Citizen or Green Card holder
- 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 Language Models or Natural Language Processing
- Experience with retrieval-augmented generation (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
Benefits
- 12.5% Annual Target Bonus
- This position is fully remote.
- Healthcare, dental, and vision insurance
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