Data Scientist

• Develops, implements, and provides production support of business intelligence reporting and sophisticated analytical model development
• Develops architecture, data availability, and data model support
• Creates scalable, efficient, and automated processes for large scale data analyses for model development, validation, and implementation
• Organize user interfaces and data visualization tools to make data analyses more effective and accessible
• Trains users to transform data into action-oriented information and to use that information to make critical business decisions
• Explores available data and discover patterns, anomalies and trends
• Develops descriptive, predictive and/or prescriptive statistical models to address business problems
• Works with large data sets in a hybrid cloud, using machine learning (ML) and artificial intelligence (AI) methods to help define requirements and set expectations
• Other duties as assigned

Business Data Scientist – Senior

• responsible for the engineering work necessary for successful creation, deployment and managing of AI capabilities of the Intelligent Delivery Platform
• ensuring data quality
• creation of new data pipelines
• optimization and management of existing data pipelines
• ingestion and curation of data sources for Gen AI purposes (including chunking/embedding strategies for RAG system)
• AI Agent delivery
• Prompt Engineering
• selection and configuration of AI-specific tools and platforms
• management and monitoring of AI models through MLOps tools and model ops practices
• operationalizing AI capabilities, working closely with larger team who will supplement where traditional application development support is needed

Lead Data Science Engineer

• 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

• 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

• Work with our team of engineers, scientists and human factors professionals to develop machine learning and other algorithm data capabilities for cybersecurity, space, and telecommunications.
• This individual contributor role will involve hands-on work collecting data, developing solutions to real-world problems using machine learning.
• Developed solutions are actively being productionized to support ongoing cybersecurity initiatives and other crucial infrastructure and security needs.
• Collaboratively develop data collection processes designing experiments to ensure quality and usefulness of data.
• Develop and evaluate machine learning solutions to interdisciplinary problems in cybersecurity and telecommunications, working with structured (time-series, tabular) and unstructured (text, 3D) data.
• Work with human factors specialists, and data/full stack engineers to define system requirements, review developed code, and support translating statistical concepts to coworkers.
• Support documentation of analysis results for semi-technical stakeholders.
• Occasionally support technical interactions like collaboration, including defining technical requirements for external data collections, and reviewing technical work.

• 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

• Apply advanced statistical techniques and predictive modeling to develop predictive maintenance strategies and optimize aircraft performance.
• Utilize natural language processing, data cleansing, and preprocessing techniques to ensure data quality and integrity for accurate analysis and modeling.
• Apply modern RAG techniques to generate domain-specific insights from complex customer data, enhancing decision-making and user experience.
• Collaborate with cross-functional teams to identify and define data requirements.
• Implement and deploy machine learning models into production systems, working closely with software engineering teams to integrate into customer environments.
• Collaborate with subject matter experts to understand engineering domain knowledge and incorporate it into data analysis and modeling efforts.
• Ensure data security and compliance with relevant regulations throughout the data analysis lifecycle.
• Communicate findings, insights, and recommendations to technical and non-technical stakeholders through clear and concise reports, presentations, and visualizations.

Mid-Level Data Scientist

• 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

• 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

• Mines and analyzes complex and unstructured data sets using advanced statistical methods for use in data driven decision making.
• Responsible for supporting cross-functional teams and providing in-depth data insights for complex business problems approached with advanced analytic techniques to collect, explore, and extract insights from structured and unstructured data.
• Develops and tests hypotheses and communicates findings in clear, precise and actionable manner to project and leadership teams.
• Works closely with teams to identify, understand, and resolve data issues and improve efficiency, productivity and scalability of data processes.
• Assists with the evaluation of data science vendors and tools.

See all jobs on JobTailor

Business Data Scientist – Senior

Lead Data Science Engineer

Data & Analytics Lead

Data Science, AI Intern

Data Scientist

Senior Data Scientist, Ecosystems

Mid-Level Data Scientist

Data Scientist, Client Data & Measurement

Data Scientist

Principal Data Scientist, Causal Inference

Senior Data Scientist

Data Scientist