Data Scientist

• Engage in and support all aspects of the HEDIS program and regulatory operations
• Drive internal and external partnerships to coordinate, guide, and oversee implementation of clinical measures
• Analyze data to solve a wide variety of business problems and create data visualizations that drive strategic vision

Senior HEDIS Data Manager

• Architect goal setting and goal decomposition mechanisms for autonomous agents operating in uncertain or open-ended environments
• Design and implement dynamic planning systems (e.g., hierarchical planning, curriculum learning, scratchpad/self-refinement loops)
• Collaborate on memory, tool-use, and feedback-loop designs enabling multi-step, self-directed agent behavior
• Develop evaluation frameworks for alignment with human intent, consistency, progress against long-horizon objectives
• Prototype agents that interface with APIs, MCP servers, search engines, databases, or real-world actuators to pursue goals safely and efficiently
• Explore mechanisms to detect and mitigate goal misalignment, looping behavior, or undesirable emergent strategies
• Work across teams to integrate goal alignment with safety, alignment, and operational reliability mechanisms

Lead Data Scientist – Autonomous Goal Management

• Lead the enterprise strategy for clinical data science, including predictive modeling, machine learning innovation, and causal evaluation of clinical programs.
• Develop and deploy advanced models that identify, stratify, and prioritize members for clinical and care management interventions.
• Oversee development of models for high-risk and rising-risk identification, avoidable utilization, chronic condition progression, and care management prioritization.
• Advance the organization’s AI capabilities by applying modern machine learning approaches, including deep learning and transformer-based models, to large healthcare datasets.
• Partner with clinical, operational, actuarial, and analytics leaders to translate business and clinical priorities into scalable data science solutions.
• Establish standards for model governance, monitoring, and performance management to ensure regulatory compliance, fairness, and responsible AI practices.
• Build, lead, and mentor a high-performing team of data scientists.

Senior Director, Clinical Data Science

• Gather, preprocess, and validate datasets from internal and external sources.
• Identify trends, patterns, and anomalies using statistical and visual techniques.
• Build and evaluate predictive models using machine learning algorithms.
• Create dashboards and visual reports to communicate findings to stakeholders.
• Maintain clear records of methodologies, code, and project outcomes.
• Work closely with cross-functional teams to align data solutions with business needs.

Data Science, AI Intern

• Collaborate with leaders across the organization to develop and execute on a data strategy inline with ActBlue's near and long term goals, including supporting organizational growth through acquisitions and partnerships
• Foster a data centric culture by upleveling our ability to make data-informed decisions and build best in class data products that meet rigorous performance and reliability standards
• Enable and advocate for AI/ML driven products and features across the ActBlue Platform through building scalable machine learning infrastructure that serves production environments and identifying new opportunities to deploy data science and analytics capacity in service of our mission
• Oversee our Data Science, Data Analytics, Data Engineering, and Data Platform teams in a fast-paced, mission-driven environment
• Guide the evolution of our data systems, tools, and architecture to meet the organization's needs, including data consolidation, governance frameworks, and unified data models across multiple entities
• Ensure high-quality communication and a stakeholder-centric approach between our data teams and their internal customers, with an emphasis on responsiveness and partnership during high-stakes moments

Director of Data Science – Engineering

• Serve as an embedded data lead:
• Understand the product – strategy, goals, roadmap, users – deeply enough to identify high-impact, strategic opportunities and risks.
• Use data to steer conversations: bring data-driven hypotheses, challenge assumptions, frame trade-offs, prioritize strategically.
• Translate vague product hunches into sharp analytical questions, and design the right approach to answer them.
• Be the source of truth for KPIs and product metrics: help define and monitor them, and identify trends and drivers.
• Drive decisionmaking with data: Prioritize analyses that meaningfully inform product decisions and change outcomes, reframe analysis requests that won’t do that into ones that will, and design and deliver the resulting analyses properly and rapidly.
• Lead experimentation thoughtfully: guide the team on when and when not to experiment, help enable self-serve experimentation, and focus on frequent, small experiments to speed learning.
• Identify where product strategy and ideas could be better, more easily, or more quickly implemented with the help of AI (ML or GenAI), and help enable this integration.
• Show up as a collaborative leader, not simply a service provider, and help focus work on what matters most.
• Work end-to-end to translate clinical, member, and claims data from our data warehouse into actionable recommendations: work with stakeholders to design reports and dashboards that answer real business questions, design the data flows to support these, and build the required data transformation pipelines.

Senior Data Scientist

• Support data collection, cleaning, transformation, and exploratory analysis for ongoing projects.
• Assist in developing, testing, and improving machine learning and LLM models.
• Perform deep-dive analysis to evaluate model behavior, output quality, and performance across different approaches.
• Help refine prompts, evaluation criteria, and testing workflows to improve consistency and outcomes.
• Partner with cross-functional teams to translate business questions into analytical solutions.
• Build reports, visualizations, and dashboards to communicate insights to technical and non-technical stakeholders.
• Document methods, experiment results, and recommendations clearly and thoroughly.
• Participate in team meetings, project discussions, and implementation reviews.

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