Data Scientist

• Leverage data science, machine learning, and advanced analytics to solve business problems and provide actionable insights.
• Plan and complete complex projects using statistical applications, big data analyses, and machine learning.
• Develop innovative analytic solutions, models, and automation processes to support commercial strategy.
• Build and support data models, algorithms, and statistical analyses for meaningful insights.
• Stay updated on emerging data engineering technologies and trends, evaluating and implementing appropriate tools.
• Collaborate with the BI team to improve reporting and visualizations for collaborators.
• Partner with the Enterprise Data & Insights team to develop and deploy data science models.
• Provide analytical commentary on industry trends and impacts.
• Present recommendations and results clearly to various audiences.
• Measure and report the impact of deployed models on business outcomes.

• 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

• Lead the design, development, deployment, and optimization of machine learning, predictive analytics, and AI-powered solutions.
• Translate business challenges and opportunities into analytical approaches, model specifications, and measurable success criteria.
• Apply advanced statistical analysis, machine learning techniques, and data science methodologies to solve complex business problems.
• Analyze large, complex datasets to identify trends, patterns, opportunities, and actionable insights.
• Develop and maintain model documentation, technical specifications, and implementation plans.
• Stay current with emerging technologies, tools, and best practices in data science, machine learning, and artificial intelligence.
• Design and execute comprehensive validation and evaluation strategies for machine learning and generative AI solutions.
• Develop benchmarking frameworks and success metrics to assess model performance, reliability, and business impact.
• Evaluate model quality using quantitative and qualitative measures, including accuracy, precision, recall, robustness, latency, and business outcome metrics.
• Assess generative AI applications for response quality, grounding, relevance, consistency, and hallucination risk.
• Identify and mitigate risks related to bias, fairness, explainability, privacy, and model reliability.
• Perform model validation, testing, and performance assessments prior to production deployment.
• Establish monitoring processes and evaluation methodologies to ensure continued model effectiveness and alignment with business objectives.
• Design, execute, and analyze experiments, including A/B tests and statistical studies, to measure product and business outcomes.
• Define key performance indicators and success metrics for machine learning and AI initiatives.
• Measure and communicate the impact of analytical solutions through statistical analysis and quantitative methods.
• Partner with stakeholders to define hypotheses, success criteria, and decision-making frameworks.
• Use experimentation and data-driven insights to guide product, operational, and strategic decisions.
• Collaborate with Engineering and Data Engineering teams to implement, operationalize, and scale models in production environments.
• Monitor deployed models for performance degradation, model drift, data quality issues, and changing business conditions.
• Recommend retraining, optimization, or replacement strategies based on model performance and evolving business needs.
• Support the creation of scalable, maintainable, and reliable AI and machine learning solutions.
• Ensure model deployment processes align with engineering best practices and operational requirements.
• Partner with Product, Engineering, Analytics, and business stakeholders to prioritize opportunities and deliver high-impact solutions.
• Communicate complex analytical findings and technical concepts to both technical and non-technical audiences.
• Present recommendations, insights, and model performance results to leadership and project teams.
• Support technical reviews, project planning, and delivery activities across cross-functional initiatives.
• Contribute to knowledge sharing, documentation, and best practices within the data science organization.
• Provide technical guidance and mentorship to junior team members and peers as needed.

Senior Data Scientist

• 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

• 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

• 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

• 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

• 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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