Data Scientist

• Identifies research problems, designs research methodologies, performs research, prepares data sets and analyses them using data science techniques, presents results for presentation to professional organizations or for scholarly publications
• Collects and analyzes complex research data, conducts experiments and interviews, and documents results according to established policies and procedures under general supervision
• Serves as a unit subject matter expert and liaison to internal and external stakeholders providing advanced level information and representing the interests of a specialized research area
• Identifies research problems and develops complex research methodologies and procedures
• May supervise the day-to-day activities of a research unit as needed
• Writes or assists in developing grant applications and proposals to secure research funding
• Attends and assists with the facilitation of scholarly events and presentations in support of continued professional development and the dissemination of research information
• Conducts literature reviews, prepares reports and materials, and disseminates information to appropriate entities
• Independently identifies and implements appropriate data science techniques to answer research questions including data set preparation, data visualization, statistical analysis, machine learning, reproducible workflows
• Publishes and presents results to help advance research
• Monitors program budget and approves unit expenditures

Clinical Data Scientist

• Analyze and model operational, economic, financial, and organizational data
• Help quantify competitive performance
• Evaluate the impact of operational changes
• Develop data-driven strategies and solutions to complex business problems
• Build modeling frameworks and predictive models for new datasets using advanced analytics
• Explore data to test hypotheses, identify important causes, and answer business questions
• Develop and run statistical programs/tests to analyze complex relationships
• Incorporate industry/competitor insights, stay current on best practices, and propose innovative solutions
• Research and maintain awareness of insurance industry best practices and our strategies

Senior Actuarial Data Scientist – Hybrid

• Provide day‑to‑day leadership, coaching, development, and performance management.
• Mentor and guide analysts, supporting onboarding, skill development, and continuous learning across career stages.
• Conduct workload planning, prioritization, and resource allocation to support multiple concurrent initiatives.
• Build and sustain a high‑performing team culture rooted in collaboration, quality, accountability, and innovation.
• Lead and oversee large scale analytical and AI initiatives, including data acquisition, transformation, modeling, AI system development, automation, and insight generation.
• Provide technical oversight and review of analytical approaches, models, and AI systems to ensure sound methodology, reproducibility, and scientific rigor.
• Guide the development and application of advanced statistical, machine learning, and AI-driven solutions, including predictive models, computer vision, large language models (LLMs), and agent-based systems.
• Lead the design and oversight of AI-enabled systems, including prompt engineering strategies, retrieval-augmented generation (RAG), embeddings, and agentic workflows.
• Establish and evolve analytical and AI best practices, documentation standards, and technical frameworks across the team.
• Ensure standards for model and AI system validation, monitoring, evaluation, documentation, and responsible AI use are consistently applied.
• Partner with engineering, IT, and data platform teams to enable scalable, reliable, and well governed solutions.
• Own delivery outcomes for data science and AI workstreams, ensuring solutions meet quality, performance, and business expectations.
• Translate business objectives into clear analytical and AI priorities, balancing near term delivery with long term capability building.
• Oversee planning, prioritization, and resourcing across projects and teams.
• Monitor solution performance, validation results, and model or AI system stability; guide troubleshooting of complex data, model, or AI issues.
• Ensure solutions are productionized effectively, with clear ownership, monitoring, and integration into business workflows.
• Establish, refine, and enforce standards for documentation, reproducibility, quality assurance, and governance.
• Remove obstacles, manage risks, and ensure consistent execution across initiatives.
• Serve as a primary point of contact for business and functional leaders on analytics and AI initiatives.
• Partner with stakeholders to define business questions, success metrics, analytical frameworks, and delivery expectations.
• Coordinate work across teams, offices, and disciplines to ensure alignment of analytical and AI approaches and outcomes.
• Communicate progress, risks, and results clearly to both technical and non technical audiences.
• Evaluate and recommend adoption of new data sources, technologies, and analytical and AI tools.
• Contribute to enterprise level analytics and AI strategy, including identifying high impact use cases and guiding their transition from concept to production.

Manager, 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

• 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

• Leverage advanced analytics and visualization tools (Power BI, Celonis) to translate complex data into actionable supply chain insights
• Lead the development and deployment of analytical models to enable data-driven decision making across the end-to-end supply chain
• Analyze large datasets to identify optimization opportunities and develop scalable metrics, KPIs, and statistical models
• Partner cross-functionally to build a robust data foundation using tools such as SQL, Snowflake, HANA VDMs, and Power BI
• Lead end-to-end initiatives including process mapping, use case identification, and project execution to drive measurable business impact and automation

• 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

• Contribute to creating best practices for the team
• Initiate and drive collaboration with stakeholders, architects, and data engineers to discover, define, cleanse, and refine the data needed for analysis and modeling
• Analyze large and novel datasets to extract actionable insights to inform model development and understand consumer behavior
• Inform experimental design to formulate solutions addressing major business challenges and innovation opportunities
• Build models using a variety of statistical and machine learning techniques, from selecting the best type of model for the problem to using techniques to measure and optimize model performance
• Prepare models for production deployment and monitoring of performance over time
• Embrace and uphold Ibotta’s Core Values of Integrity, Boldness, Ownership, Teamwork, Transparency, & A good idea can come from anywhere

Data Scientist, Client Data & Measurement

• Documents approaches to address research questions and contributes to the establishment of reproducible research methodologies and analysis workflows
• Composes and assembles reproducible workflows and reports to clearly articulate patterns to researchers and/or administrators
• Independently identifies and implements appropriate data science techniques to find data patterns and answer research questions including data visualization, statistical analysis, machine learning, and data mining
• Organizes and automates project steps for data preparation and analysis
• Prepares data sets for analysis including cleaning/quality assurance, transformations, restructuring, and integration of multiple data sources

Data Scientist II

• Leverage a broad range of techniques to turn data into actionable business insights
• Tackle complex analytical problems and uncover trends
• Operate in a fast-paced environment managing projects end-to-end
• Work with stakeholders to identify opportunities for leveraging company data
• Mine and analyze data to drive optimization and improvement
• Develop custom data models and algorithms
• Communicate business requirements to ensure buy-in from all departments
• Participate in projects to provide data and analyses as needed

• Contribute to cutting-edge research in the field of health informatics.
• Develop and execute high-impact machine learning and statistical modeling projects.
• Use machine learning and computational methods for data analysis.
• Provide data for abstract and manuscript submissions.
• Assist with the development of abstracts, posters, presentations, and manuscripts.
• Involved in project monitoring and evaluation, and dissemination of program results.

• Help lead the continued evolution of Ibotta's measurement methodology through exploration of cutting-edge measurement research and experimental design
• Collaborate as the go-to troubleshooter for measurement anomalies—exploring outlier results, diagnosing data quality issues, and validating statistical assumptions across alpha, beta, and production measurement systems
• Lead code reviews and architecture discussions, providing expert guidance on design patterns, scalability, and technical trade-offs
• Foster a culture of code quality, rigorous measurement, and collaborative problem-solving
• Evaluate and adopt new tools, explore frameworks and innovative model forms that enhance team effectiveness, while maintaining code quality standards
• Lead enterprise-wide data science initiatives spanning 6+ months with measurable business impact across multiple teams and KRs
• Build trusted partnerships with C-suite executives, product managers, and data science leaders to translate business problems into technical solutions while delivering with consistency and transparency
• Present complex technical concepts clearly to both technical and non-technical audiences, including executive leadership
• Deliver high impact presentations on key initiatives and research, with easily understood visualizations, to drive insight and adoption
• Mentor more junior data scientists through technical guidance, code reviews, and strategic coaching
• Create technical training programs and documentation that elevate organizational data science maturity
• Embrace and uphold Ibotta's Core Values: Integrity, Boldness, Ownership, Teamwork, Transparency, & A good idea can come from anywhere

Principal Data Scientist, Causal Inference

• Leverages machine learning and predictive modeling projects across a variety of problem domains or business initiatives.
• Manipulates and investigates large and complex datasets.
• Works closely with business partners and domain experts to identify critical questions.
• Collaborates with domain experts to develop problem definitions, analytical approaches, and research designs.
• Participates in the selection of research tools, design processes, and brainstorming new methodologies.
• Researches and maintains awareness of industry best practices and business strategies.
• Proactively brings in new and innovative ideas and approaches.

Actuarial Data Scientist

• 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

• Dataset creation & curation
• Build high-quality labeled datasets from operational data sources including structured databases, event logs, sensor streams, and document repositories
• Define feature engineering strategies for time-series, event-based, and unstructured data
• Predictive model development
• Build, validate, and maintain predictive models (e.g. anomaly detection, classification, forecasting) that serve as callable tools within agentic AI systems
• Apply rigorous statistical methods: hypothesis testing, cross-validation, and confidence interval estimation to ensure model outputs are trustworthy when surfaced by an agent
• Agent data interfaces & RAG grounding
• Own the data pipeline that populates structured knowledge bases used for retrieval-augmented generation in agentic products
• Build evaluation frameworks to measure retrieval quality and factual accuracy against domain-specific ground-truth datasets.
• Experimentation & statistical rigor
• Apply relevant causal inference techniques (e.g. synthetic controls, difference-in-difference) to isolate causal effects in operational environments
• Serve as the statistical conscience of the AI team: design measurement frameworks before shipping, and build internal culture around responsible AI performance claims
• Cross-functional enablement
• Collaborate with product managers to translate domain use cases into well-formed ML problem statements
• Work with AI engineers and data platform teams to align on feature store standards and machine learning best practices that support reliable agent tool integration.

Senior Data Scientist – Agentic AI Products

• Serve as a technical data lead on product teams leveraging automation and AI/ML technologies to solve real-world government challenges
• Lead the design of data architectures for enterprise-scale solutions
• Partner with internal stakeholders and client teams to define requirements, identify gaps, and craft differentiated data solutions
• Research and apply emerging technologies in AI/ML, data analytics, cloud, cybersecurity, agile development, DevSecOps, and digital engineering
• Contribute to the development of technical concepts for written proposal content (RFIs, RFPs, white papers, etc.) and deliver technical presentations to clients and partners
• Provide technical thought leadership to customers and internal teams
• Collaborate with SMEs and product teams to define architecture, implementation approaches, and operational concepts for federal solution programs

Federal Data Scientist – Automation, Integration

• 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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