Data Scientist

• 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

• Identify and define opportunities aligned to Water business challenges
• Drive experimentation and delivery of digital AI / data science product innovations
• Work within an agile global resource model
• Network and collaborate with internal business stakeholders
• Provide clear, consistent communication and presentation to various product stakeholders
• Mentor junior data scientists in the organization
• Utilize an extensive foundation of data science principles

Senior Data Scientist

• Serve as the primary point of contact for At Work-related data and analytics needs
• Partner with CAPTRUST’s centralized data team to prioritize, design, and improve data solutions supporting At Work
• Help establish consistent definitions, metrics, and reporting standards across the At Work practice
• Improve data quality, clarity, and usability over time
• Support and enhance internal and client-facing reporting used by At Work teams
• Work closely with Operations to understand recurring reporting needs and reduce one-off or manual efforts
• Develop dashboards and reports that support operational planning, advisor effectiveness, and leadership decision-making
• Translate reporting outputs into insights that inform action, not just describe activity
• Develop qualitative and quantitative measures of client and participant engagement, trust, and relationship health
• Help establish ongoing feedback loops across the client and participant lifecycle, beyond episodic surveys
• Identify trends that signal strengthening relationships, stagnation, or potential breakdowns
• Support analysis related to the annual financial wellness survey and other experience-related initiatives
• Analyze patterns associated with strong long-term outcomes such as sustained engagement, retention, and expansion of advisory relationships as needs evolve
• Partner with leadership and Operations to identify where additional investment of time or resources may be needed
• Help reduce reliance on anecdotal feedback through clearer, shared insight

Data & Analytics Lead

• To partner with stakeholders to identify, scope, and execute advanced analytics and data science projects
• To design data modeling processes, create algorithms, and predictive models
• To share insights that inform and enable business decisions
• Builds, prototypes, and deploys machine learning models in production environments
• Collaborates with product, technology, and other stakeholders to identify and scope data science projects
• Communicates modeling challenges, findings, and outcomes to technical and non-technical stakeholders
• Designs and implements end-to-end ETL (extract, transform, load) data pipelines integrating data from multiple data sources
• Explores new internal and external data sources and assesses their commercial benefit
• Mentors team members and serves as a subject matter expert in cross-functional discussions

Senior AI Data Scientist

• Lead analytical support for portfolio strategy and enterprise initiatives
• Build and maintain advanced analytical models to support scenario development
• Analyze large, complex datasets to identify trends, key drivers, risks, and opportunities
• Develop clear metrics, scorecards, and business cases that support strategic and investment decisions
• Prepare high-quality Excel and PowerPoint deliverables that convey insights and support decision-making
• Provide ad hoc analysis for leadership requests, internal reviews, and strategic initiatives
• Manage multiple concurrent projects independently, prioritizing effectively and meeting deadlines
• Partner cross-functionally to drive action

Corporate Real Estate Portfolio Strategy Lead

• Advising and providing mentorship and guidance to junior data analysts.
• Advocating for best-in-class processes and techniques for statistical analysis, modeling, and Big Data capabilities.
• Fostering effective working relationships and data-fueled solutions that drive the business toward its goals.
• Ensuring statistical integrity, accuracy, and adequacy within the department.
• Innovating, exploring, and practicing the art of ‘the possible’ to uncover valuable data science opportunities and bring them to fruition.
• Conducting research, designing, implementing, and deploying scalable data analytics and machine learning solutions.
• Dissecting complex problems and finding data-driven solutions; addressing various business issues by leveraging diverse data sources.
• Developing and maintaining an analytics infrastructure for widespread use.
• Delivering advance AI and GenAI models to enable desired business outcomes.
• Performing development at all stages in the data science lifecycle; working autonomously and taking ownership of projects and driving them forward.
• Clearly communicating findings to technical and non-technical enterprise stakeholders in addition to various levels of organizational leadership.

Principal Data Scientist

• Help power the next generation of final-mile logistics.
• Develop advanced models and data systems that leverage location, routing, traffic, and spatial signals to improve delivery success, reduce operational costs, and create a better experience for customers and drivers.
• Work with large-scale GPS traces, mapping data and geospatial datasets.
• Collaborate with product managers, logistic operators and software engineers to turn models into real-world systems.
• Design experiments and translate complex geospatial insights into actionable operational decisions.

• Evaluate and understand current & nascent modelling methods in the Data Science space
• Maintain, expand, and ensure the data quality of natural language
• Train and evaluate Large Language Models on a variety of topics
• Work closely with business stakeholders to deliver new Natural Language Processing & Artificial Intelligence products
• Develop new features for existing algorithms through statistical and machine learning methodologies
• Validate and implement modeling improvements to increase accuracy of data science Algorithms
• Embrace an R&D mindset through Rapid Prototyping & Iterative Experimentation
• Work closely with Tech delivery teams to productionize successful models in an on-demand environment

• Manage a high performing team spanning multiple products/areas - monitor health, sustainability, cross team dynamics, value delivered
• Drive development of algorithm capabilities in the supply chain space
• Own team outcomes and project success & impact
• Work with product leadership team to identify, scope & assign tasks and projects
• Engage in higher-level business processes (Quarterly Business Reviews, etc) and advocate for team success
• Be a voice of expertise in algorithm design/architecture and data science knowledge and know-how, while working to grow junior team members as subject-matter experts
• Embrace an R&D mindset through Rapid Prototyping & Iterative Experimentation
• Serve as an escalation point for supervisory staff and complex business requests
• Develop strategic relationships with the business to understand goals and strategy to help influence and bring data strategies to life
• Partner with Talent Acquisition to establish a pipeline, recruit, select, and onboard qualified and diverse talent while owing team hiring efforts
• Manage annual merit and equity programs for assigned team to align compensation with performance results
• Outline employee role accountabilities and expectations; manage performance to drive the right behaviors and improve results and provide useful real-time coaching, feedback, and mentoring, development activities and stretch assignments; support employees with problem resolution
• Encourage employee feedback through formal and informal channels
• Other duties or responsibilities as assigned according to the team and/or country specific requirements

Senior 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

• Support modeling, data analysis, campaign execution, and database needs for assigned line of business
• Manage large data sets, providing information-based decision logic and predictive modeling solutions
• Assist in the development and implementation of complex analytical solutions leveraging tools such as predictive modeling, advanced machine learning techniques
• Partner closely with leader and others to ensure proper delivery
• Consult and coordinate campaign execution for direct to client campaigns
• Collaborate with business leaders and provide analytical thought leadership and support for business problems

• Design, build, and maintain predictive models supporting customer expansion including propensity-to-upgrade and engagement scoring models
• Use behavioral, usage, firmographic, and lifecycle data to identify signals indicating readiness for AI adoption and expansion
• Partner with Lifecycle Marketing to translate model outputs into actionable segments and testable hypotheses across the customer lifecycle
• Collaborate with Marketing Operations to operationalize models within Customer Data Platform (Treasure Data) for activation and measurement
• Develop features and datasets using product usage data, campaign engagement, learning activity, and customer attributes
• Validate, monitor, and continuously improve model performance ensuring accuracy, explainability, and alignment to business outcomes
• Support experimentation by defining success metrics, analyzing lift, and interpreting results for optimization decisions
• Document modeling approaches, assumptions, and outputs for transparency and cross-functional understanding
• Work cross-functionally with Data Engineering, Product, Customer Success, and Commercial teams to ensure data quality and aligned outcomes

Data Scientist, Customer & Growth Analytics

• Leverage machine learning and Generative AI (LLMs) to deliver data-driven solutions
• Build and deploy predictive and LLM-based models using modern tools (CI/CD, Airflow)
• Analyze and integrate large, complex datasets from multiple sources
• Monitor model performance, maintain documentation, and ensure governance
• Develop dashboards and visualizations using Power BI to track KPIs and model outcomes
• Mentor junior team members and contribute to best practices

Data Scientist II, Advanced 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

• Developing advanced marketing measurement and modeling capabilities
• Partnering with business leaders, analysts, and scientists
• Technical mentoring of junior data scientists
• Collaborating with technology leaders, product owners, and engineers
• Contributing to product development cycles in an agile environment
• Assisting with technical parts of business development as needed
• Building strong relationships across the organization

Senior Data Scientist – Measurement & Modeling Development

• Understand and execute analytic plans with the appropriate statistical or modeling technique.
• Conduct analyses supporting existing and new product development or customer sales opportunities.
• Assemble, merge and parse large amounts of data to detect meaningful trends and patterns.
• Explore opportunities to enhance existing products with new features or new data sources.
• Develop machine learning models, create model code, and work with internal or external stakeholders to validate accuracy of production code.
• Interpret, document, and communicate analytic work to non-technical audiences.
• Proactively identify and communicate data quality issues and successfully work with other teams to implement solutions.
• Collaborate with cross-functional peers to define product and data science strategies.
• Provide support to other data scientists across the organization.
• Critical reviews of data experiments to ensure accuracy, completeness, and feasibility.

Data Scientist III

• 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

• 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

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