Data Scientist

• Lead the design, development, and implementation of advanced machine learning, generative AI solutions across structured and unstructured data
• Build and deploy scalable, automated data science products and reusable analytic assets that support business and client objectives
• Analyze large-scale, high-dimensional datasets using modern analytics tools and distributed computing platforms to identify trends, opportunities, and actionable insights
• Design and prototype agentic AI workflows leveraging large language models (LLMs), retrieval systems, structured data, APIs, tools, and business rules to automate complex business processes
• Translate business requirements into AI agent architectures, including task decomposition, tool orchestration, routing logic, escalation paths, and human approval checkpoints
• Optimize retrieval-augmented generation (RAG) solutions through embedding evaluation, metadata design, reranking approaches, citation quality assessment, and knowledge freshness validation
• Develop evaluation frameworks for machine learning and AI solutions, including performance, accuracy, reliability, hallucination risk, latency, cost, safety, and consistency measures
• Make key analytical and architectural decisions, establish technical standards, and provide leadership on methodology, model selection, solution design, and AI implementation best practices
• Lead code reviews, mentor data scientists, and promote best practices in software development, responsible AI, model governance, and analytics delivery
• Partner with engineering, platform, and business teams to deploy, monitor, and maintain production-ready analytics and AI solutions
• Translate complex analytical findings into clear reports, visualizations, and presentations for technical and non-technical audiences
• Collaborate with stakeholders and clients to define analytical approaches that address strategic, operational, and clinical objectives
• Evaluate emerging technologies and industry trends to advance organizational analytics and AI capabilities.

Lead Data Scientist

• Lead implementation of fraud risk strategies for consumer and commercial card businesses with decision engine systems in accordance with defined change control procedures and controls.
• Own and manage fraud rules, scores, and detection strategies, risk appetite execution, POS interdiction strategies and defect analysis.
• Lead gathering of business requirements, testing, and implementation of projects impacting fraud decision systems as well as system incidents.
• Leverage analytics to identify enhancement opportunities and more granular insights that can be acted upon, while ensuring adherence to Fraud Policy.
• Build effective relationships within and outside the Fraud organization to help ensure successful and timely execution of key portfolio priorities.
• Collaborate with cross-functional teams to provide strategy recommendations based on data and trend analysis and implement mitigation strategies.
• Continuously improve processes and strategies by exploring and evaluating new data sources, tools, and capabilities
• Leverage customer data to build risk segmentation/ mitigation strategies and complete complex analyses to identify strategy and procedure gaps, manage implementation process across several systems to affect change.
• Lead key analytical projects within the card first party fraud analytics team and support the Card lines of business by utilizing advanced predictive analytical and statistical techniques.
• Translate data into consumer or customer behavioral insights to drive targeting and segmentation strategies, and communicate clearly and effectively to business partners and senior leaders all findings
• Work closely with internal and external business partners in building, implementing, tracking, and improving fraud capture decision strategies.
• Refine framework for change control risk assessment including evaluation of control gaps within existing processes.
• Supports Governance process, evaluating historical performance and emerging changes in the environment.
• Prioritize and provide a clear line of sight to the most critical work to partners and team members.
• Mentor and coach junior team members.

VP Fraud Risk Business Analytics Lead

• 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

• Engage in and support all aspects of the HEDIS program and regulatory operations
• Drive internal and external partnerships to coordinate, guide, and oversee implementation of clinical measures
• Analyze data to solve a wide variety of business problems and create data visualizations that drive strategic vision

Senior HEDIS Data Manager

• Architect goal setting and goal decomposition mechanisms for autonomous agents operating in uncertain or open-ended environments
• Design and implement dynamic planning systems (e.g., hierarchical planning, curriculum learning, scratchpad/self-refinement loops)
• Collaborate on memory, tool-use, and feedback-loop designs enabling multi-step, self-directed agent behavior
• Develop evaluation frameworks for alignment with human intent, consistency, progress against long-horizon objectives
• Prototype agents that interface with APIs, MCP servers, search engines, databases, or real-world actuators to pursue goals safely and efficiently
• Explore mechanisms to detect and mitigate goal misalignment, looping behavior, or undesirable emergent strategies
• Work across teams to integrate goal alignment with safety, alignment, and operational reliability mechanisms

Lead Data Scientist – Autonomous Goal Management

• 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

• Actionable Insight: Generate compelling and actionable insights from complex, multi-source marketing data sets that directly inform channel investment, campaign design, and pipeline strategy.
• Stakeholder engagement: Establish strong collaborative relationships with marketing leaders and operators across demand gen, partner, field, product marketing, and brand, delivering high-impact analytics initiatives that translate loose, evolving requirements into clear deliverables.
• Attribution & Measurement: Design, build, and maintain the attribution framework for UpGuard – spanning first and multi-touch attribution and incrementality testing – and clearly communicate the trade-offs and assumptions behind each lens.
• Channel & Program Analytics: Develop a deep, first-principles understanding of channel logic across paid media, organic, content, lifecycle, partner, and field, and build the metrics, models, and dashboards that let each program owner self-serve their performance.
• Data Products: Partner with the data engineering team to design, construct, and maintain foundational marketing data assets – translating loose marketing requirements into well-specified dbt models that the whole organisation can rely on.
• Business Intelligence: Partner strategically with marketing stakeholders to provide robust self-service and conversational analytics capabilities, using design thinking principles to build user-friendly dashboards for funnel performance, channel ROI, partner sourced pipeline, and field event attribution.
• Deep Dive analysis: Personally conduct thorough, hands-on, technical analysis to diagnose and solve the most significant marketing challenges – from channel saturation and diminishing returns to lead quality decay and campaign cannibalisation.
• Commercial Analysis: Provide ongoing operational support to the commercial growth of the organisation, connecting marketing investment to pipeline, ARR, and payback in ways that finance and the executive team trust.

Marketing Analytics Lead

• Apply machine learning and natural language processing to financial crime challenges
• Develop a thorough understanding of the business landscape
• Navigate intricate scenarios to enhance personal and technical growth
• Leverage SQL and Python for data analysis and problem-solving

Senior Associate, AML and Sanctions – Data Scientist

• Work From Anywhere: Senior Revenue Cycle Analytics & Data Science Expert (Remote)
• Join our team in a fully remote capacity.
• Leverage complex data to guide high-level business decisions.
• Focus on leveraging data science—including predictive and prescriptive analytics—to tackle high-value operational challenges.
• Develop, test, and deploy standard reports and certified datasets to provide clear, actionable information.

Senior Revenue Cycle Analytics, Data Science Expert

• 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

• Leads the design, development, validation, deployment, and lifecycle management of end-to-end predictive/prescriptive analytics solutions (e.g., forecasting, anomaly detection, optimization, risk scoring, early-warning systems).
• Owns problem framing with business and operational stakeholders; translates ambiguous needs into measurable objectives, success metrics, analytical requirements, and delivery roadmaps.
• Selects best-fit methodologies (e.g., statistical modeling, machine learning, deep learning, NLP, computer vision, time series, simulation, optimization) and defines modeling approaches, evaluation strategies, and governance.
• Drives data preparation and feature engineering for complex, multi-source datasets; establishes repeatable pipelines for data quality, lineage, and model inputs.
• Establishes and enforces modeling and engineering standards (code quality, peer review, documentation, reproducibility, bias/robustness checks, monitoring, retraining triggers).
• Leads technical reviews (design, algorithm, code, and model risk reviews) and provides guidance to other data scientists and partner teams.
• Partners cross-functionally with analytics, engineering, quality, safety, operations, and product/IT teams to integrate solutions into business workflows and decision systems.
• Influences analytics strategy for the organization, including platform/tooling recommendations, model deployment patterns, experimentation/measurement approaches, and reuse of common assets.
• Monitors deployed solutions (performance drift, data drift, operational KPIs) and drives continuous improvement through iteration, retraining, and user feedback.
• Mentors and develops junior data scientists; actively contributes to knowledge sharing, technical communities, and capability building across the organization.
• Communicates complex technical outcomes clearly to senior leadership, including tradeoffs, risks, assumptions, and expected business impact.

Senior/Lead Data Scientist

• - Provide technical contributions in a fast-paced R&D team environment to accelerate our efforts on building new analytics.
• - Utilize advanced mathematical models, machine learning algorithms, operations research techniques, and strong business acumen to deliver insight, recommendations, and solutions.
• - Analyze data sets and develop models to predict fraud, waste, and abuse.
• - Document and present results to internal team members for development and inclusion in company products and analysis.
• - Satisfies all applicable requirements imposed by applicable Company customers, clients, or business partners, with respect to the relationship and role.
• - Understands and complies with all company Privacy and Security standards.
• - This job requires access to confidential and sensitive information, requiring ongoing discretion and secure information management.

• 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

• Design, build, and deploy agentic AI solutions for sales, customer service, procurement, and operations using Azure OpenAI, Copilot Studio, and Semantic Kernel.
• Develop, validate, and deploy ML models for business use cases including demand forecasting, classification, anomaly detection, and optimization.
• Implement retrieval-augmented generation architectures on Microsoft tooling, including chunking strategies, embedding models, vector search, and hybrid retrieval.
• Own the ML lifecycle end-to-end: experiment tracking, model registration, deployment, monitoring, and retraining using Azure ML with CI/CD in Azure DevOps.
• Develop prompt engineering strategies, evaluate LLM outputs, and implement guardrails to minimize hallucination and ensure accuracy in production.
• Partner with data engineering teams and SaaS vendors (Canals.ai, Fragment.ai) to improve data pipelines, evaluate vendor model performance, and build proprietary capabilities where warranted.
• Collaborate with AI Product Owners and the GenAI team to translate business problems into data science workstreams, provide technical guidance for Copilot Studio makers, define success metrics, and validate business value.
• Present model results, AI capabilities, and recommendations in terms that non-technical business leaders understand and can act on.
• Effectively collaborate and communicate with all team SMEs.

Senior Data Scientist

• 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

• Collaborate with cross-functional teams to identify and define data requirements, data collection methods, and data quality standards
• Build systems that enable conducting data analysis and exploration to identify patterns, trends, and insights that drive decision-making
• Provide technical leadership to team of data scientists
• Implement and deploy machine learning models, 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
• Continuously evaluate and improve existing models and algorithms to enhance their accuracy, efficiency, and scalability
• Ensure data security, privacy, and compliance with relevant regulations and policies throughout the data analysis lifecycle
• Stay up-to-date with the latest advancements in data science, machine learning, and aerospace technologies to drive innovation and maintain a competitive edge
• Communicate findings, insights, and recommendations to technical and non-technical stakeholders through clear and concise reports, presentations, and visualizations
• Mentor and provide guidance to junior data scientists
• Contribute to the development of best practices and standards for data science within the organization

• 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

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