Data Scientist

• Develop, enhance, and maintain predictive models (e.g., GLMs, machine learning, ensemble approaches) to support pricing, segmentation, and performance forecasting while balancing regulator considerations.
• Apply advanced statistical techniques to identify key drivers of sales, retention, and profitability, and forecast expected outcomes.
• Serve as a technical lead on larger, cross-functional initiatives.
• Lead scoped projects and drive them independently to completion, escalating questions or blockers appropriately.
• Manage a rotating portfolio of multiple active projects, including pricing model builds, research initiatives, and support for regulatory filings.
• Lead exploratory data analysis to evaluate relationships between data elements and insurance processes, including pricing, conversion, retention, and risk segmentation.
• Perform advanced root cause analysis to investigate data inconsistencies and system issues; collaborate with Data Engineering and Business Units to resolve findings.
• Translate complex technical findings into clear, actionable insights for leadership with varying technical backgrounds.
• Design, implement, and maintain scalable data pipelines to collect, process, and integrate structured and unstructured data from internal systems and external data sources.
• Create and maintain dashboards and reporting solutions (e.g., Power BI) to monitor key metrics such as rate level, risk segmentation, retention, and portfolio performance.
• Mentor and develop analytics talent within the team and across the organization.
• Stay current on industry trends, emerging technologies, and advancements in data science, analytics, and actuarial science.

Senior Data Scientist

• Develop, implement, and deploy machine learning models and statistical analyses to optimize energy storage and solar asset performance across Stem's portfolio
• Design and build data pipelines to extract, transform, and analyze large-scale datasets from energy systems
• Collaborate with software engineering, product management, and energy experts to integrate data-driven solutions into PowerTrack platforms
• Create data visualizations, dashboards, and reports to communicate insights and recommendations to stakeholders
• Conduct exploratory data analysis to identify trends, patterns, and opportunities for improving system performance and business outcomes

Data Scientist, Forecasting – Energy Storage System Optimization

• 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

• 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

• Translate complex business challenges into practical, data-driven solutions using statistical analysis, machine learning, simulation, and optimization techniques.
• Design and deliver scalable solutions that support internal decision-making and power customer-facing applications.
• Leverage expertise in databases, cloud platforms, and programming to develop advanced analytical models.
• Collaborate with peers to enhance internal tools and expand the organization’s library of data science capabilities.
• Gathers, interprets, and manipulates structured and unstructured data to enable advanced analytical solutions for the business.
• Develop scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
• Select the appropriate modeling technique and/or technology considering data limitations, application, and business needs.
• Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Communicate outcomes to non-technical business colleagues with focus on business action and recommendations.

Data Scientist I

• Collaborate with business stakeholders to understand data needs and objectives.
• Extract, clean, and preprocess data from various sources for analysis.
• Develop predictive models, statistical analyses, and algorithms to solve complex business problems.
• Build tools to automate data collection.
• Create and maintain interactive dashboards and reports using Power BI.
• Perform ad-hoc data analysis to support decision-making.
• Conduct data exploration and visualization to identify trends and patterns.
• Stay up-to-date with the latest industry trends and best practices in data analysis and Power BI.

• 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

• 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

• Directs and consults on analytical projects that leverage vast amounts of structured and unstructured data to extract actionable business insights.
• Directs the data gathering, data processing and data mining of large and complex datasets.
• Provides deep technical expertise to advance the data science capabilities of analytics teams.
• Serves as a representative of PNC; understands the needs and challenges PNC is facing and provides thought leadership around data science activities both internally and externally.
• Supervises the implementation of advanced analytics projects, and as an expert in the field, helps other data scientists present the analytical processes and outcomes to management.
• Partners with Data Architects, Data Analysts, Data Engineers and Visualization Experts to develop data-driven solutions for the business.

Lead Data Scientist

• Leads the implementation of analytical projects that leverage vast amounts of structured and unstructured data to extract actionable business insights
• Directs the data gathering, data processing and data mining of large and complex datasets
• Leads the development of algorithms using advanced mathematical and statistical techniques like machine learning to predict business outcomes and recommend optimal actions to management
• Leads analytical experiments in a methodical manner to find opportunities for product and process optimization
• Presents business insights to management using visualization technologies and data storytelling
• Partners with Data Architects, Data Analysts, Data Engineers and Visualization Experts to develop data-driven solutions for the business

• Develop predictive and prescriptive models to support pricing, risk, customer, and operational decision-making across the pet insurance portfolio
• Apply advanced statistical and machine learning techniques to a variety of business problems, including customer behavior, retention, and marketing performance
• Mentor other data scientists and analytics talent as needed, and develop data science rigor and expertise across the organization
• Partner with actuarial, product, and analytics teams to set data science goals and objectives and translate business questions into analytical solutions
• Collaborate with engineering and analytics teams to deploy models into production and ensure scalable, maintainable solutions
• Communicate insights and recommendations to technical and non-technical audiences, leveraging learnings from insurance and other consumer-facing industries

• 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

• Develop, enhance, and maintain predictive models (e.g., GLMs, machine learning, ensemble approaches) to support pricing, segmentation, and performance forecasting while balancing regulator considerations.
• Lead exploratory data analysis to evaluate relationships between data elements and insurance processes, including pricing, conversion, retention, and risk segmentation.
• Apply advanced statistical techniques to identify key drivers of sales, retention, and profitability, and forecast expected outcomes.
• Translate complex technical findings into clear, actionable insights for both technical and non-technical audiences through presentations, reports, and visualizations.
• Perform advanced root cause analysis using complex SQL and analytical techniques to investigate data inconsistencies and system issues; collaborate with Data Engineering and Business Units to resolve findings.
• Design, implement, and maintain scalable data pipelines to collect, process, and integrate structured and unstructured data from internal systems and external data sources.
• Lead data science projects end-to-end, owning analytical scope, methodology, timelines, and deliverables.
• Serve as a technical lead on larger, cross-functional initiatives.
• Create and maintain dashboards and reporting solutions (e.g., Power BI) to monitor key metrics such as rate level, risk segmentation, retention, and portfolio performance.
• Mentor and develop analytics talent within the team and across the organization.
• Stay current on industry trends, emerging technologies, and advancements in data science, analytics, and actuarial science.

• Translates business problems into applied statistical, machine learning, simulation, and optimization solutions to inform actionable business insights and drive business value through automation, revenue generation, and expense and risk reduction.
• In collaboration with engineering partners, delivers solutions at scale, and enables customer-facing applications.
• Leverages database, cloud, and programming knowledge to build analytical modeling solutions using statistical and machine learning techniques.
• Collaborates with other data scientists to improve USAA's tooling, expanding the company's library of internal packages and applications.
• Works with model risk management to validate the results and stability of models before being pushed to production at scale.
• Gathers, interprets, and manipulates structured and unstructured data to enable advanced analytical solutions for the business.
• Develops scalable, automated solutions using machine learning, simulation, and optimization to deliver business insights and business value.
• Selects the appropriate modeling technique and/or technology with consideration to data limitations, application, and business needs.
• Develops and deploys models within the Model Development Control (MDC) and Model Risk Management (MRM) framework.
• Composes technical documents for knowledge persistence, risk management, and technical review audiences.
• Assesses business needs to propose/recommend analytical and modeling projects to add business value.
• Participates in the prioritization of analytics and modeling problems/research efforts with business and analytics leaders.
• Contributes to the development of a robust library of reusable, production-quality algorithms and supporting code, to ensure model development and research efforts are transparent and based on the highest quality data.
• Translates business request(s) into specific analytical questions, executes on the analysis and/or modeling, and then communicates outcomes to non-technical business colleagues with focus on business action and recommendations.
• Works closely with Data Engineering, IT, the business, and other internal stakeholders to deploy production-ready analytical assets that are aligned with the customer's vision and specifications while being consistent with modeling best practices and model risk management standards.
• Maintains awareness of cutting-edge techniques. Actively seeks opportunities and materials to learn new techniques, technologies, and methodologies.
• Ensures risks associated with business activities are effectively identified, measured, monitored, and controlled in accordance with risk and compliance policies and procedures.

• Participate in collection, conversion, and assembly of data in a variety of formats
• Assist in the analysis of data to help clients improve efficiency of operations
• Assist in development of data collection systems and databases
• Collect and organize data from different sources
• Discover trends and patterns, combine various algorithms and modules
• Use machine learning tools to select features and optimize classifiers
• Work with team members to develop a data strategy
• Develop dashboards and reporting tools for decision makers
• Support recruitment, mentorship, and cross-training of staff

• 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

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