Data Scientist

• Define Ads ontologies and metrics for campaigns, budgets, bids, creatives, audiences, and placements.
• Build dbt models and curated marts in Snowflake with clear data contracts, tests, and SLOs.
• Ingest and enrich unstructured Ads content and publish retrieval‑ready datasets using our managed search/vector services.
• Design and evaluate retrieval workflows (RAG) with existing services for hybrid search and re‑ranking; set quality/latency targets and iterate via experiments.
• Design agent reasoning and policies on ads, including tool definitions and human‑in‑the‑loop approvals.
• Establish evaluation suites covering precision/recall, calibration, hallucination rate, latency, and cost.
• Run A/B or uplift experiments to quantify impact and guide iteration.
• Translate Ads problems into agent behaviors and own KPIs such as ROAS lift, pacing accuracy, RCA precision/recall, forecast MAPE, and time‑to‑insight.

Ads AI Analytics Lead

• Own clinical data quality across claims, EHR, and ADT: Define standards for how clinical data is structured, normalized, and validated as modeling inputs across payer claims (medical, pharmacy, eligibility), EHR data (Epic, Cerner, Athena), and real-time ADT feeds. Bring deep familiarity with EHR data formats (FHIR, HL7, C-CDA) and how data from systems like Epic, Cerner, and Athena maps to clinical reality. Hold the bar for clinical accuracy and completeness across all three sources.
• Build and ship production ML/AI models: Develop, validate, and deploy risk stratification, care gap prediction, treatment effect estimation, and LLM/foundation model applications — with rigor around leakage, calibration, fairness, and clinical face validity.
• Apply health economics and outcomes methods: Translate raw clinical and claims data into decision-grade evidence through risk adjustment, utilization measurement, cost attribution, quasi-experimental evaluation, and outcomes measurement aligned with CMS, NCQA, and MCO reporting standards.
• Advance machine and AI products: Bring senior modeling judgment to the product roadmap, owning the clinical and methodological soundness of what ships.
• Set standards and mentor: Make architectural trade-offs, drive alignment across data science, engineering, product, and clinical stakeholders, and mentor junior data scientists to raise the technical bar of the team.

Principal Data Scientist, Health Informatics

• Partner with Marketing to Define and Solve Problems – Work closely with marketing stakeholders to understand business challenges, define success metrics, and translate needs into analytical approaches that drive performance across campaigns and channels.
• Design and Deliver Data-Driven Solutions – Apply statistical analysis and machine learning to develop solutions that address business needs, then present findings, influence decisions, and gain alignment on adoption.
• Lead Experimentation and Optimization – Develop and manage testing frameworks (A/B testing, campaign experimentation) across channels and markets. Analyze results and provide clear recommendations to improve performance and inform future strategy.
• Translate Results into Business Impact – Clearly communicate insights and quantify outcomes (e.g., campaign performance lift, engagement improvements, ROI) to ensure stakeholders understand the value and take action.
• Partner to Operationalize Solutions – Collaborate with Technology and Engineering teams to transition validated models into production, supporting implementation through scalable pipelines and processes.
• Influence Through Storytelling – Present insights and recommendations to both technical and non-technical audiences, including senior stakeholders, to drive alignment and decision-making.

Senior Data Scientist – Marketing Analytics

• Develop research methods and data analysis plans related to healthcare utilization, cost trends, financial forecasting, member, and provider experience.
• Expertly use appropriate software tools to retrieve, edit and tabulate data from various databases and files.
• Conduct descriptive, prescriptive, predictive analyses.
• Summarize complex data for leadership in the appropriate form (e.g., statistical summaries, narratives, charts, tables, graphs, etc.) to convey meaning, significance, and actionable information.
• Contribute/Lead in the research and development of models, algorithms, and analysis tools to mature the use and creation of self-service data products at CareOregon.
• Develop visual reporting (e.g., dashboards, scorecards, etc.) using data visualization tools such as Tableau when appropriate.
• Develop appropriate subject matter, domain expertise and serve as an expert resource.
• Collaborate with other subject matter experts to identify opportunities for building repeatable data products.
• Collaborate with business teams to understand business needs and to illicit and document requirements for analytics, algorithms, models, etc.
• Develop reporting tools to comply with regulatory reporting requirements.
• Review and assess completeness, validity, and reliability of internal data used for analysis and reporting.
• Create and maintain technical documentation such as data dictionaries, business glossaries, metric definitions, and other data resources.
• Thoroughly document models, algorithms, data products, processes and any analysis, reports developed.
• Contribute to, and actively participate in, community of practice to improve the skills and capabilities of other analysts at CareOregon.
• Assist with, prepare, and/or deliver presentations to community groups, regulatory agencies, leadership, and other teams as needed.

Data Science Analyst II

• Actionable Insight: Generate compelling and actionable insights from complex, multi-source marketing data sets that directly inform channel investment, campaign design, and pipeline strategy.
• Stakeholder engagement: Establish strong collaborative relationships with marketing leaders and operators across demand gen, partner, field, product marketing, and brand, delivering high-impact analytics initiatives that translate loose, evolving requirements into clear deliverables.
• Attribution & Measurement: Design, build, and maintain the attribution framework for UpGuard – spanning first and multi-touch attribution and incrementality testing – and clearly communicate the trade-offs and assumptions behind each lens.
• Channel & Program Analytics: Develop a deep, first-principles understanding of channel logic across paid media, organic, content, lifecycle, partner, and field, and build the metrics, models, and dashboards that let each program owner self-serve their performance.
• Data Products: Partner with the data engineering team to design, construct, and maintain foundational marketing data assets – translating loose marketing requirements into well-specified dbt models that the whole organisation can rely on.
• Business Intelligence: Partner strategically with marketing stakeholders to provide robust self-service and conversational analytics capabilities, using design thinking principles to build user-friendly dashboards for funnel performance, channel ROI, partner sourced pipeline, and field event attribution.
• Deep Dive analysis: Personally conduct thorough, hands-on, technical analysis to diagnose and solve the most significant marketing challenges – from channel saturation and diminishing returns to lead quality decay and campaign cannibalisation.
• Commercial Analysis: Provide ongoing operational support to the commercial growth of the organisation, connecting marketing investment to pipeline, ARR, and payback in ways that finance and the executive team trust.

Marketing Analytics Lead

• 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

• Analyze product usage data to identify behavioral patterns, segmentation opportunities, and growth drivers
• Develop predictive models to analyze customer churn, retention, expansion, and product adoption
• Build customer health scores and predictive signals to support proactive customer success strategies
• Apply appropriate AI/ML techniques (e.g., advanced segmentation, pattern detection, NLP where relevant) to enhance insights
• Translate business questions into analytical frameworks and data science solutions
• Partner with Customer Success and Product teams to identify opportunities to improve engagement and reduce churn
• Generate actionable insights and recommendations to influence business decisions
• Communicate complex analyses clearly to both technical and non-technical stakeholders
• Collaborate with BI teams to embed predictive insights into dashboards and reporting

Senior Data Scientist, Customer Analytics

• Own the proprietary model flywheel with 90M+ assigned programs and build a proprietary therapeutic AI model
• Lead AI product delivery across all AI bets and drive design, development, deployment, and continuous improvement of AI systems
• Define and execute the AI/ML technical roadmap by collaborating with Product and Content to prioritize clinical and product needs
• Establish trust-by-design as a standard by ensuring transparency and auditability in all AI features
• Own the clinical AI safety and quality framework, ensuring HIPAA-compliant data handling alongside Legal
• Drive cross-functional alignment and work closely with various teams to embed AI in workflows
• Continuously evaluate emerging developments in LLMs and clinical AI for practical advantages

Head of Data and Machine Learning

• Bridge the gap between complex datasets and strategic action.
• Synthesize information from various external sources to develop visibility and insights into search recall gaps and emerging topic trends.
• Inform Wikimedia’s strategy for ensuring that people continue to find the content they need.
• Integrate fragmented data to create a unified view of how users discover and use content.
• Translate analytics and complex trends into actionable narratives.
• Develop automated systems to detect under-indexed content.
• Establish metrics and frameworks for content utilization within Large Language Models.
• Collaborate with product leaders to set goals and track success.
• Assess efficacy of new initiatives through experiment design and analysis
• Interpret website performance data to inform UX/UI enhancements.

Senior Data Scientist, Ecosystems

• End-to-end ownership of Search, Recommendations, and Personalization
• Identify high-impact opportunities
• Rapidly prototyping and experimenting (often using AI tools)
• Measuring and iterating based on real business outcomes
• Develop and iterate on models (ranking, recommendation, personalization, etc.)
• Partner with Product, Engineering, and Analytics to bring ideas to life

Senior/Lead Applied Data Scientist

• Lead and develop a team of Data Scientists
• Define the measurement and experimentation strategy
• Elevate the craft of experimentation
• Partner closely with Product, Marketing, Sales, and Finance
• Translate complex quantitative signals into clear insights
• Increase team leverage by building scalable analytics systems
• Operate effectively in ambiguity

Senior Data Science Manager

• 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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