Data Scientist

• Own enterprise data science strategy and multi-year roadmap.
• Prioritize high-impact use cases with executive stakeholders.
• Oversee model lifecycle, from discovery to productionization.
• Establish enterprise level standards for MLOps and responsible AI governance.
• Develop talent, structure teams, and manage budgets.
• Communicate results and risks to senior leadership.

Senior Director, Data Science

• Design, build, train, evaluate, and deploy machine learning and generative AI solutions.
• Develop and maintain predictive analytics, NLP, computer vision, and LLM-based applications.
• Implement Retrieval-Augmented Generation (RAG), agentic workflows, and knowledge management solutions.
• Deploy and operate local/open-source models in secure environments.
• Design and deploy AI solutions within Azure Government.
• Develop data pipelines supporting AI and analytics workloads.
• Implement CI/CD pipelines for machine learning and AI workloads.
• Collaborate with mission owners, analysts, engineers, cybersecurity personnel, and leadership.

Senior Data Scientist, AI Engineer

• 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

• Provide advanced analytics expertise within the Business Analytics function
• Independently apply data science techniques to solve business problems
• Develop, validate, and maintain predictive models and AI solutions
• Utilize multiple sources of data and various machine learning techniques
• Write clean, modular, and well-documented code for production
• Interpret data and model outputs to generate actionable insights

Senior Data Scientist – Level I

• Lead the design, development, and deployment of advanced statistical, machine learning, and AI solutions—including LLM-powered applications—to solve complex business, workforce, and organizational challenges.
• Translate ambiguous business and HR questions into well-defined analytical approaches, scalable data products, and decision-support tools.
• Design and oversee end-to-end data science workflows, including data extraction (e.g., enterprise data warehouses), validation, modeling, deployment, and performance monitoring.
• Integrate data from multiple internal and external sources to create modeling-ready datasets, reusable data assets, semantic layers, and metadata frameworks that enable scalable and self-service analytics.
• Develop and productionize predictive and prescriptive models to explain outcomes, forecast behavior, and identify risks and opportunities.
• Build and deploy advanced AI solutions using modern frameworks (e.g., LLMs, embeddings, RAG architectures), and lead experimentation and rapid prototyping to evaluate emerging capabilities.
• Embed analytics and AI solutions into business processes through automation, system integration, and near real-time data capabilities.
• Partner with HR Business Partners, talent leaders, executives, data engineering, and IT teams to deliver actionable insights and ensure alignment with architectural, security, and data quality standards.
• Provide technical leadership across data science initiatives, ensuring consistency with best practices, methodologies, and quality standards.
• Communicate complex analytical insights and AI concepts clearly to non-technical stakeholders, influencing strategic and operational decision-making.
• Mentor and coach junior team members, elevating team capabilities in AI, machine learning, and analytics best practices.
• Ensure adherence to responsible AI principles, including data privacy, bias mitigation, security, and ethical use of employee data.
• Stay current on emerging AI and analytics trends, proactively identifying opportunities to incorporate new technologies into enterprise use cases.

Senior Data Scientist – AI, People Analytics

• Accountable for identifying, shaping and delivering strategic results through multiple business analytics disciplines.
• Works in collaboration with business leaders to provide thought partnership and drive decision making based on data and analytical insights across different parts of the organization.
• Consistently has a seat at the table with the leaders they support to influence the strategic direction of the organization and solve the most complex, highest priority problems.
• Able to make analytically sound decisions, often based on limited information, that inform solutions for business priorities.
• Reporting to the head of the Provider Operational Health team, responsible for providing strategy and analytics support for the operation.
• Lead multiple Strategy and Analytics teams supporting the PX&O organization, working closely with Operations partners to deeply understand the business problems at hand.
• Guide the development of analytical models/insights and build compelling business cases that positively influence outcomes.
• Develop business cases, secure funding for prioritized strategic initiatives and coordinate strategic efforts with broader enterprise business partners.
• Build and maintain strong partnerships with cross functional organizations within operations, tech, product, and business partner teams.

Business Analytics Lead

• 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 enterprise strategy for clinical data science, including predictive modeling, machine learning innovation, and causal evaluation of clinical programs.
• Develop and deploy advanced models that identify, stratify, and prioritize members for clinical and care management interventions.
• Oversee development of models for high-risk and rising-risk identification, avoidable utilization, chronic condition progression, and care management prioritization.
• Advance the organization’s AI capabilities by applying modern machine learning approaches, including deep learning and transformer-based models, to large healthcare datasets.
• Partner with clinical, operational, actuarial, and analytics leaders to translate business and clinical priorities into scalable data science solutions.
• Establish standards for model governance, monitoring, and performance management to ensure regulatory compliance, fairness, and responsible AI practices.
• Build, lead, and mentor a high-performing team of data scientists.

Senior Director, Clinical Data Science

• Lead the analytics center of excellence for Gainsight's GTM organization, with an initial focus on Sales and Customer Success
• Set standards for dashboard design, metric definitions, data governance, and insight delivery across the team
• Own the quality and trust of all GTM reporting, ensuring leadership can rely on the numbers without second-guessing them
• Act as a mentor to peer analysts, focused on technical and business acumen, escalation support, knowledge and skills sharing
• Own end-to-end analytics across bookings, pipeline health, forecasting, GRR, NRR, renewals, and customer expansion
• Partner with the GTM Planning team on evolution of planning models that support AOP, quota setting, capacity planning, and scenario analysis
• Deliver executive-ready dashboards and weekly/monthly/quarterly operating cadences for Sales and CS leadership
• Partner with Finance on bookings-to-revenue reconciliation and board-level reporting
• Proactively surface risks and opportunities, moving from answering questions to anticipating them
• Lead the transformation from static BI dashboards to AI-enabled, self-service data exploration for GTM stakeholders
• Partner with the GTM Data Architect to establish the semantic layer — the business-side definitions of ACV, GRR, pipeline, and churn that enable reliable AI querying
• Design and deploy AI-powered analytical workflows including natural language querying, anomaly detection, and proactive insight surfacing
• Enable Sales and CS leaders to query data independently without routing every question through the analytics team
• Stay current on the evolving landscape of AI analytics tooling and bring forward recommendations
• Serve as a trusted analytical thought partner to Sales VPs, CS leadership, and RevOps
• Translate complex data into clear business recommendations, connecting the numbers to the "so what"
• Support strategic planning processes including territory design, AOP, and Rules of Engagement

Senior Revenue Analytics Lead

• The Director, Data Science & AI Engineering will lead the development and execution of the Firm’s enterprise data science, analytics, and AI engineering strategy.
• This role is responsible for building and managing a multidisciplinary team of Data Scientists, Data Analysts, and MLOps / AI Engineers focused on delivering innovative, production-ready AI solutions that support the Firm’s legal and business operations.
• Key responsibilities include overseeing the design, implementation, and optimization of LLM-powered applications, retrieval-augmented generation (RAG) systems, AI agents, custom models, advanced analytics, and business intelligence platforms that provide actionable matter, financial, and operational insights to firm leadership.
• This position serves as a strategic and hands-on leadership role within a rapidly evolving AI environment, responsible for establishing scalable architecture, governance, evaluation standards, security practices, and operational frameworks appropriate for a highly regulated professional services organization.

Director, Data Science – AI Engineering

• The Enterprise Data Lead is a newly created, high-visibility role established to serve as the business champion for data governance, data quality, data definitions, and process standards across Helen of Troy.
• Address significant legacy systems and data debt by shifting accountability from a technical problem to a process and behavioral solution.
• Drive behavior change across all brands, regions, and corporate functions, ensuring data accurately reflects commercial and operational realities.
• Serve as the primary Business Champion for data governance, data quality, data definitions, and data processes partnering with IT.
• Partner with Domain Owners and IT to align recommendations regarding governance models, data architecture changes, and strategic policy updates; Act as a Domain Owner for Item Data across the company.
• Define and enforce standard enterprise data definitions and business rules within the technical constraints of the ERP platform to maximize operational efficiency.
• Lead the development, optimization, and modernization of core enterprise data processes (e.g., new item and customer setup workflows) to remove operational bottlenecks.

Enterprise Data Lead

• Work From Anywhere in a fully remote capacity.
• Leverage advanced analytics to tackle operational challenges including denials management and AR optimization.
• Develop, test, and deploy standard reports and certified datasets to drive accountability among business leaders.
• Report to the Director Analytics System.

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

• Define and own the enterprise data science vision, roadmap, and operating model aligned with company strategy
• Translate business priorities into high-impact analytics, AI, and machine learning initiatives
• Serve as an executive advisor on data-driven decision-making, AI adoption, and emerging technologies
• Build, mentor, and scale a diverse, high-performing organization of data scientists and ML engineers
• Oversee the design, development, deployment, and lifecycle management of predictive and prescriptive models
• Maintain a hands-on approach to data science and AI, including the ability to write, review, and guide code
• Work closely with Data Engineering to ensure data quality, availability, and scalability

VP Data Science

• Key areas of accountability include participating in the design and development of Data and Analytic Solutions including GenAI as part of Strategic Cybersecurity projects and programs;
• Providing input into technology roadmaps and application evolution plans;
• Acting as a researcher and evaluator of new technology, information, or integration standards.
• Conduct Data Discovery, data preparation and data processing for business intelligence and ML / AI Models.
• Exploring, promoting, and implementing semantic data capabilities through data analytics and machine learning techniques.
• Leading to define requirements and scope of data analyses; presenting and reporting possible business insights to management using data visualization technologies.
• Conducting research on data model optimization and algorithms to improve effectiveness and accuracy on data analyses.
• Plan technical deliverables and definition of done for each sprint for the Jr. Data Engineers.
• Communicate and Present the Technical Deliverables to stakeholders and business in an easy-to-understand way.
• Demonstrate a breadth of knowledge in the application statistical methods and/or digital methods to solve business problems.
• Develop, validate, train and implement statistical models, and implement digital solutions.
• Participate on 3-4 projects/products concurrently.
• Demonstrate strong initiative to research and apply new methods to exceed customer expectations.
• Have a strong focus on continual learning in the Analytics field.

Lead Data Scientist

• 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

• Define and deliver the enterprise BI and reporting strategy aligned to business priorities.
• Establish a clear vision for self-service analytics, standardized reporting, and insight generation.
• Drive the transition from reactive reporting to proactive, insight-led decision support.
• Own and govern all business-critical dashboards, KPIs, and management reporting across the organization.
• Lead the delivery of high-impact, ad hoc analysis and insight requests from senior stakeholders.
• Translate complex data into clear, compelling narratives and recommendations.
• Act as a trusted advisor to executive and senior business stakeholders.
• Build, lead, and develop a high-performing team of BI analysts and data visualization specialists.
• Ensure reporting integrity through strong alignment with data governance and data quality frameworks.
• Own and optimize the BI tooling ecosystem (e.g., Power BI, Tableau, etc.).

Head of Data Insights

• Leverage advanced analytics and visualization tools (Power BI, Celonis) to translate complex data into actionable supply chain insights
• Lead the development and deployment of analytical models to enable data-driven decision making across the end-to-end supply chain
• Analyze large datasets to identify optimization opportunities and develop scalable metrics, KPIs, and statistical models
• Partner cross-functionally to build a robust data foundation using tools such as SQL, Snowflake, HANA VDMs, and Power BI
• Lead end-to-end initiatives including process mapping, use case identification, and project execution to drive measurable business impact and automation

• 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

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