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

• Provide advanced analytics expertise within the Business Analytics function
• Independently apply data science techniques to solve business problems
• Develop, validate, and maintain predictive models and AI solutions
• Utilize multiple sources of data and various machine learning techniques
• Write clean, modular, and well-documented code for production
• Interpret data and model outputs to generate actionable insights

Senior Data Scientist – Level I

• Lead the design, development, and deployment of advanced statistical, machine learning, and AI solutions—including LLM-powered applications—to solve complex business, workforce, and organizational challenges.
• Translate ambiguous business and HR questions into well-defined analytical approaches, scalable data products, and decision-support tools.
• Design and oversee end-to-end data science workflows, including data extraction (e.g., enterprise data warehouses), validation, modeling, deployment, and performance monitoring.
• Integrate data from multiple internal and external sources to create modeling-ready datasets, reusable data assets, semantic layers, and metadata frameworks that enable scalable and self-service analytics.
• Develop and productionize predictive and prescriptive models to explain outcomes, forecast behavior, and identify risks and opportunities.
• Build and deploy advanced AI solutions using modern frameworks (e.g., LLMs, embeddings, RAG architectures), and lead experimentation and rapid prototyping to evaluate emerging capabilities.
• Embed analytics and AI solutions into business processes through automation, system integration, and near real-time data capabilities.
• Partner with HR Business Partners, talent leaders, executives, data engineering, and IT teams to deliver actionable insights and ensure alignment with architectural, security, and data quality standards.
• Provide technical leadership across data science initiatives, ensuring consistency with best practices, methodologies, and quality standards.
• Communicate complex analytical insights and AI concepts clearly to non-technical stakeholders, influencing strategic and operational decision-making.
• Mentor and coach junior team members, elevating team capabilities in AI, machine learning, and analytics best practices.
• Ensure adherence to responsible AI principles, including data privacy, bias mitigation, security, and ethical use of employee data.
• Stay current on emerging AI and analytics trends, proactively identifying opportunities to incorporate new technologies into enterprise use cases.

Senior Data Scientist – AI, People Analytics

• 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

• Lead the development of advanced statistical, machine learning, and AI solutions that support strategic and operational decision-making.
• Apply domain and business expertise to translate complex problems into well-defined analytical approaches and scalable solutions.
• Design and oversee end-to-end data science workflows, including data extraction, validation, modeling, deployment, and performance monitoring.
• Integrate data from multiple internal and external sources to produce modeling-ready datasets and reusable data assets.
• Build predictive and prescriptive models to explain outcomes, forecast behavior, and identify risk or opportunity.
• Play a lead role in embedding analytics and AI solutions into existing business processes through automation and system integration.
• Partner with data engineering, IT, and business teams to ensure solutions meet architectural, security, and quality standards.
• Provide technical leadership across data science initiatives, ensuring consistency with agreed-upon methodologies and quality attributes.
• Communicate analytical results and implications clearly to leaders, enabling informed decision-making.
• Coach and mentor less experienced data scientists; provide guidance on prioritization, approach, and technical execution.
• Ensure adherence to responsible AI standards, including privacy, bias mitigation, and ethical use of employee data.
• Maintain awareness of emerging analytics and AI techniques and evaluate applicability to enterprise use cases.

• 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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