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

• As a Data Scientist in Manufacturing Analytics, you will use your knowledge of data and advanced analytics to identify and articulate the role data and analytics products play in helping the business achieve their goals.
• You will develop analytics products using your expertise in visualization, AI/ML, Statistics and Optimization using GDI&A approved packages and architectures.
• You will collaborate with Data Engineers and Software Engineers to develop robust analytics products.
• You will use your knowledge of the product driven operating model, analytic and software delivery via Google Cloud Platform to optimize the delivery of value.
• You will interact with business partners in Manufacturing to align with their needs and processes to ensure relevancy of products.
• Accelerate the application of value-added analytics and machine learning into the portfolio of products for Manufacturing Analytics.
• Drive analytic excellence into product teams by collaborating with Data Scientists, Data Engineers and Software Engineers in analytic and machine learning methods.
• Work closely with the Product Manager and Product Owner to translate Business Value needs into analytic deliverables and, where appropriate, software products for delivery by product teams.
• Work hands-on with the team and other partners to deliver solutions that meet our customer's requirements and needs.
• Act as a consultant to the business vs. an order taker.
• Balance 'doing it right' with 'speed to delivery' by identifying and mitigating risk, generating options, educating business and other decision makers, and taking on justified technical debt.

Data Scientist, Manufacturing 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

• 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

• Identifies business trends and problems through complex data analysis.
• Interprets results from multiple sources using a variety of techniques, ranging from simple data aggregation via statistical analysis to complex data mining independently.
• Designs, develops, and implements the most valuable business solutions for the organization.
• Leverage skills in handling very large datasets to perform exploratory analysis, construct relevant variables, and perform sample design
• Build and improve upon already built, complex predictive models from multiple sources to substantially improve business operations
• Automate feedback loops for algorithms/models in production and create repeatable processes and scalable data products
• Act as technical expert to recommend and develop innovative analytic solutions
• Communicate and present results to internal audiences, prepare presentations and documentation of models and analytical solutions
• Influence functional teams and develop best practices across the organization
• Other duties as assigned

Senior Data Scientist

• Design, develop, and optimize advanced predictive models using a broad range of statistical, machine learning, and actuarial techniques
• Perform deep feature engineering, variable selection, and transformation to improve model stability, interpretability, and performance
• Evaluate and compare candidate models using rigorous validation techniques
• Balance model accuracy, robustness, and explainability to ensure business usability and regulatory acceptance
• Apply predictive models to influence complex business problems such as pricing, risk evaluation, customer lifetime value, demand forecasting, and operational efficiency
• Quantify business impact through financial metrics
• Translate modeling outputs into clear recommendations and decision strategies for senior leaders
• Own model documentation, assumptions, limitations, and versioning in alignment with internal governance standards
• Partner with model validation, audit, and risk teams to support model reviews and respond to findings
• Develop and maintain model performance monitoring frameworks

• 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

• Lead hands-on Generative AI experimentation, including prompt engineering, prompt library development, and agent-style workflows that support voice-of-customer understanding, issue identification, and decision support.
• Design and execute systematic testing of LLM outputs across large collections of historical customer interaction data, evaluating behavior across tasks, data conditions, and edge cases.
• Conduct deep error analysis of GenAI outputs, identifying hallucinations, weak or missing evidence, false positives, false negatives, and ambiguity, and translate findings into targeted prompt and system improvements.
• Develop and apply GenAI evaluation frameworks, including rule-based heuristics, statistical indicators, and LLM-as-a-Judge techniques, to assess output quality, consistency, and risk.
• Build and refine confidence and uncertainty scoring mechanisms for LLM decisions to support prioritization and secondary human review in higher-risk scenarios.
• Apply machine learning and NLP models where appropriate to complement GenAI solutions, such as feature extraction, classification, clustering, or signal generation.
• Analyze complex structured and unstructured datasets to generate hypotheses, surface emerging risks, and identify opportunities where GenAI can augment or automate decision workflows.
• Collaborate closely with product teams, engineers, and business stakeholders to align GenAI experimentation with operational workflows, risk tolerance, and real-world constraints.
• Produce clear documentation of prompts, experiments, evaluation methods, and findings to ensure transparency, repeatability, and knowledge sharing.
• Communicate GenAI behaviors, trade-offs, limitations, and risks effectively to non-technical stakeholders, helping set appropriate expectations for usage.
• May mentor teammates by sharing best practices related to GenAI experimentation, evaluation, and responsible deployment.

Senior Data Science Consultant – Enterprise Complaints, Remediations, Loudspeaker

• 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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