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

• 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

• Develop a breakthrough idea or solution for customer’s pressing problems
• Collaborate with teams in a startup accelerator environment
• Receive mentorship from senior leaders
• Participate in final Challenge Cup presentation

Data Scientist Intern, Summer 2026

• Deploy Cutting-Edge Data Science & Integration Innovation from API-led integration ecosystems to orchestrated operational workflows to advanced ML pipelines directly at customer sites in defense, intelligence, and aerospace environments.
• Become a Trusted Partner Embedded with Mission Owners, working in classified and unclassified environments to deeply understand operational challenges and translate them into end-to-end data science and integration solutions using best-fit tooling.
• Drive Innovation at the Front Lines by serving as the connective bridge between customer missions and internal engineering teams, gathering frontline feedback to help co-design integration patterns, workflow templates, and data science capabilities that strengthen national security outcomes.
• Improve Efficiency, Mission-Readiness, and Trust by helping the U.S. and allied governments deploy integrated data technology stacks that deliver faster response times, better personnel utilization, and more effective logistics and asset management.
• Drive Tangible Outcomes through Hands-On Implementation, taking responsibility for end-to-end technical delivery of complex data pipelines, integration flows, and predictive models—personally writing critical code, configuring systems, and troubleshooting issues in the field while guiding junior team members.
• Build Transformative Data Science Solutions by taking primary ownership of designing, developing, and implementing high-quality, scalable production ML and analytics systems, including predictive models, intelligent automation pipelines, and LLM-augmented workflows.
• Engineer Bespoke Integration Architectures, designing and building custom API-led integrations using enterprise integration platforms to ensure seamless connectivity across enterprise data sources, operational systems, and cloud environments in highly regulated settings.
• Orchestrate Complex Workflows, leveraging modern workflow automation and process orchestration platforms to model, automate, and optimize multi-stakeholder operational processes in manufacturing, logistics, supply chain, and mission operations contexts.
• Own the Entire Data Lifecycle on-site with customers—from strategic data modeling to hands-on ETL/ELT pipeline construction, feature engineering, and integration strategy—ensuring data is always ready, optimized, and secure for advanced analytics and AI applications.
• Lead Rapid Prototyping and Iteration, developing proofs-of-concept and MVPs side-by-side with customer teams to demonstrate data science and integration capabilities and set the pace for rapid, mission-aligned delivery.

Forward Deployed Engineering Lead – Data Science, Integration

• 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

• Define and evolve the measurement strategy for Zillow’s Elevate on-site advertising platform, including ad placements, pricing models, inventory optimization, and advertiser value.
• Lead incrementality, lift testing, and experimentation frameworks that support advertiser reporting and internal decision-making.
• Build KPI frameworks that connect ad performance to advertiser outcomes, product decisions, and revenue growth.
• Partner with Ad Product, Ad Tech, Sales, Commercial, and Data Engineering teams to ensure measurement solutions support both monetization and advertiser trust.
• Translate ad performance and marketplace data into actionable insights that shape product roadmaps and go-to-market strategy.
• Collaborate with third-party measurement vendors, platform providers, and data partners to strengthen Zillow’s advertising measurement capabilities.
• Drive cross-functional alignment on experimentation design, measurement standards, and scalable analytical best practices across the ads ecosystem.
• Mentor data scientists, improve analytical systems and data reliability, and identify responsible AI-enabled workflows that increase efficiency while maintaining rigor and governance.

Principal Data Scientist

• Utilize data analytics tools to support the design and implementation of claim fraud detection and prevention initiatives.
• Compiles, cleans, organizes and analyzes complex data to understand fraud results.
• Develop and maintain comprehensive data models to identify fraud patterns and trends.
• Pilot technical solutions to enhance both operational and data analysis capabilities for fraud detection and prevention.
• Collaborate with internal partners such as IT, Legal, Compliance, and Claims to ensure alignment and effective fraud mitigation solutions.
• Design and operate reports to measure the effectiveness and loss cost containment of fraud mitigation tools, processes, and initiatives.
• Build automated self-service reports to track referrals, assignments, performance metrics, and outcomes of claim fraud.
• Develop a consistent communication strategy to keep all stakeholders informed and engaged.
• Develop and maintain strong relationships with key departments to foster a culture of transparency and trust.
• Ensure timely and effective communication with all relevant parties during fraud investigations.

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