Note: The job is a remote job and is open to candidates in USA. Pager Health redefines how members navigate to care and wellness, validated by clinicians and powered by AI. They are seeking a Senior Data Scientist to lead high-priority, cross-functional data science and AI initiatives that drive measurable business impact across their products and operations.
Responsibilities
- Lead the design, development, deployment, and optimization of machine learning, predictive analytics, and AI-powered solutions
- Translate business challenges and opportunities into analytical approaches, model specifications, and measurable success criteria
- Apply advanced statistical analysis, machine learning techniques, and data science methodologies to solve complex business problems
- Analyze large, complex datasets to identify trends, patterns, opportunities, and actionable insights
- Develop and maintain model documentation, technical specifications, and implementation plans
- Stay current with emerging technologies, tools, and best practices in data science, machine learning, and artificial intelligence
- Design and execute comprehensive validation and evaluation strategies for machine learning and generative AI solutions
- Develop benchmarking frameworks and success metrics to assess model performance, reliability, and business impact
- Evaluate model quality using quantitative and qualitative measures, including accuracy, precision, recall, robustness, latency, and business outcome metrics
- Assess generative AI applications for response quality, grounding, relevance, consistency, and hallucination risk
- Identify and mitigate risks related to bias, fairness, explainability, privacy, and model reliability
- Perform model validation, testing, and performance assessments prior to production deployment
- Establish monitoring processes and evaluation methodologies to ensure continued model effectiveness and alignment with business objectives
- Design, execute, and analyze experiments, including A/B tests and statistical studies, to measure product and business outcomes
- Define key performance indicators and success metrics for machine learning and AI initiatives
- Measure and communicate the impact of analytical solutions through statistical analysis and quantitative methods
- Partner with stakeholders to define hypotheses, success criteria, and decision-making frameworks
- Use experimentation and data-driven insights to guide product, operational, and strategic decisions
- Collaborate with Engineering and Data Engineering teams to implement, operationalize, and scale models in production environments
- Monitor deployed models for performance degradation, model drift, data quality issues, and changing business conditions
- Recommend retraining, optimization, or replacement strategies based on model performance and evolving business needs
- Support the creation of scalable, maintainable, and reliable AI and machine learning solutions
- Ensure model deployment processes align with engineering best practices and operational requirements
- Partner with Product, Engineering, Analytics, and business stakeholders to prioritize opportunities and deliver high-impact solutions
- Communicate complex analytical findings and technical concepts to both technical and non-technical audiences
- Present recommendations, insights, and model performance results to leadership and project teams
- Support technical reviews, project planning, and delivery activities across cross-functional initiatives
- Contribute to knowledge sharing, documentation, and best practices within the data science organization
- Provide technical guidance and mentorship to junior team members and peers as needed
Skills
- Bachelor's degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, or a related quantitative field; Master's degree preferred
- 7+ years of experience in data science, machine learning, advanced analytics, or a related field
- Demonstrated experience developing and deploying machine learning models in production environments
- Strong foundation in statistics, hypothesis testing, experimental design, and predictive modeling
- Experience working with large datasets and distributed data processing environments
- Proficiency in Python, SQL, and common data science and machine learning frameworks
- Experience communicating analytical findings and recommendations to business and technical stakeholders
- Proven ability to lead projects and collaborate effectively across cross-functional teams
- Experience developing and evaluating generative AI, LLM, RAG, or AI agent solutions
- Experience designing model evaluation frameworks and benchmarking methodologies
- Familiarity with MLOps practices, model monitoring, and production AI systems
- Experience with cloud platforms such as AWS, Azure, or Google Cloud
- Experience in healthcare, healthcare technology, digital health, or other regulated industries
- Knowledge of responsible AI principles, model explainability techniques, and bias mitigation approaches
Benefits
- Stock options
- A range of medical, dental, vision, financial, generous PTO, stipends for professional development, and wellness benefits
- Remote-first
- Stipends for professional development
- Wellness benefits
Company Overview
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