Data Scientist

• Frame ambiguous revenue-cycle problems as tractable modeling problems and own them end to end such as claim and invoice viability scoring, denial and underpayment prediction, revenue/ARR prediction, and work prioritization from hypothesis to a validated, monitored result in production
• Build and validate models with statistical rigor: thoughtful feature design, sound handling of missingness and class imbalance, calibration, honest evaluation against simple baselines, and a clear treatment of uncertainty
• Design, build, and maintain the data pipelines feeding these models integrating with source collections, billing, and EHR systems and internal data stores, and handling data collection, cleansing, field mapping, and payer/plan normalization with a bias toward reliability and data quality
• Establish and own MLOps practice: model monitoring and drift detection (e.g., population stability), calibration and threshold selection, benchmarking against simple baselines, explainability (e.g., SHAP and interpretable coefficients), experiment and version tracking, and model documentation such as model cards
• Partner with subject-matter experts to encode business-rule and heuristic logic alongside statistical models where that produces a more accurate or more defensible result
• Gather and document business requirements for revenue-cycle initiatives, including workflows, decision points, stakeholder objectives, operational constraints, success criteria, data availability, and underlying business assumptions, and maintain traceability as those requirements evolve.

Senior AI Data Scientist

• Lead the development and delivery of enterprise reporting, executive dashboards, KPI scorecards, portfolio analytics, competitor benchmarking, and strategic business insights.
• Conduct in-depth analysis of industry trends, competitive dynamics, emerging technologies, market opportunities, and regulatory developments affecting the consumer lending, credit card, banking, payments, and fintech industries.
• Apply advanced statistical techniques, machine learning models, and predictive analytics to identify patterns, forecast market changes, and support strategic business decisions.
• Design and implement AI-powered market intelligence and competitive research solutions that automate the collection, synthesis, and interpretation of market data.
• Utilize Large Language Models (LLMs) to analyze and summarize earnings calls, SEC filings, analyst reports, press releases, industry publications, and competitor disclosures.
• Develop Retrieval-Augmented Generation (RAG) solutions and AI agents that enable continuous monitoring of competitor activity, customer trends, market signals, and regulatory developments.
• Build executive AI copilots that support strategic planning, competitive analysis, and business decision-making.
• Integrate external market, economic, customer, and industry datasets with internal business performance data to generate comprehensive insights.
• Analyze portfolio performance across lending products, identifying opportunities related to growth, profitability, customer behavior, risk management, and operational efficiency.
• Partner with business, product, marketing, digital, risk, technology, and finance leaders to translate analytical findings into actionable business recommendations.
• Support board presentations, regulatory requests, audit reviews, second-line risk reviews, and executive strategic initiatives through rigorous analysis and communication.
• Prepare audit-ready analyses and documentation to support regulatory, compliance, and governance requirements.
• Present complex analytical findings and strategic recommendations to executive leadership in a clear, concise, and compelling manner.
• Foster a culture of innovation, analytics excellence, data-driven decision-making, and AI adoption across the organization.

Director, Data Science

• Responsible for mining big data, interpreting and analyzing complex data sets
• Develops and deploys predictive models to analyze company performance and improve customer experiences
• Applies statistical techniques and machine learning algorithms to identify trends within Mazda data
• Collaborates closely with data engineers, analysts, and business stakeholders to integrate data-driven solutions

• Manage project deliverables, including developing scope of work and scheduling project reports
• Manage the development of drawings for clients and property/facility management team
• Proactively manage project-related issues as necessary
• Ensure success in initiation, planning, execution, and completion of all projects against agreed upon targets
• Maintain high qualitative and quantitative standards for work performance
• Prepare agendas and meeting materials, lead meetings, and develop and maintain master project budgets and timelines
• Advise and obtain necessary approvals from management of potential organizational changes
• Coordinate and track all vendor RFQ’s and RFP’s
• Review all change orders and other invoices associated with the project

Project Manager, Project & Development Services – Data Center

• Own the roadmap for data enablement, customer communications platforms, and eMarketing tooling across marketing needs, lifecycle functions, and emerging conversion use cases
• Translate complex data needs from marketing and business partners into clear product requirements that engineering can build against
• Partner with data engineering and analytics teams to identify gaps in data availability, data quality, and segmentation capability and convert those gaps into backlog items
• Define use cases, features, requirements, and acceptance criteria for communications platforms including notifications, content management, and campaign tooling
• Lead cross-functional initiatives that span marketing, technology, and lifecycle teams, bringing structure to ambiguous problems and driving alignment across stakeholders
• Use sound judgment to assess risks and produce well-structured, data-informed written narratives for leadership
• Analyze product and platform performance and establish metrics that reflect both technical health and business value
• Manage vendor relationships where third-party platforms such as communications vendors and CMS tools are core to the product surface
• Maintain strong communication channels across marketing, engineering, and operations, translating strategy into execution and keeping stakeholders informed on timelines, blockers, and outcomes

Product Manager – Data Enablement and Communications

• Buіld, mаіntаіn, аnd іmрrоvе dесіѕіоn ѕуѕtеmѕ аnd рrеdісtіvе mоdеlѕ thаt аrе kеу tо Рrоgrеѕѕіvе Lеаѕіng’ѕ соmреtіtіvе аdvаntаgе
• Еxpлоrе nеw dаtа ѕоurсеѕ bоth іntеrnаl аnd ехtеrnаl tо Рrоgrеѕѕіvе аnd uѕе fіndіngѕ tо furthеr іmрrоvе mоdеl реrfоrmаnсе
• Dеѕіgn, іmрlеmеnt, аnd rеаd іn-mаrkеt ехреrіmеntѕ tеѕtіng nеw аlgоrіthmѕ, tесhnоlоgіеѕ, аnd рrосеѕѕеѕ
• Рrоtоtуре ѕоlutіоnѕ tо buѕіnеѕѕ рrоblеmѕ uѕіng сuttіng-еdgе tесhnоlоgіеѕ
• Rеcоmmеnd аnd ѕuрроrt ѕtrаtеgіс сhаngеѕ thrоugh rіgоrоuѕ аnаlуtісѕ, сrеаtіvе рrоblеm ѕоlvіng
• Іdеntіfу, еvаluаtе, аnd hеlр іmрlеmеnt аdvаnсеd аnаlіtісѕ mеthоdѕ
• Bе паrt оf а соllеgіаl tеаm оf lіkе-mіndеd dаtа ѕсіеntіѕtѕ!

• This position is primarily for educational or religious purposes and should be used for providing valuable educational experiences for the Intern.
• Under general supervision, the Intern performs work as assigned by the department.
• Responsibilities may vary by assignment depending on department and need.
• The Intern receives assignments from supervising staff. The Manager reviews the Intern's work occasionally.
• The Intern is expected to meet rigid and challenging deadlines, work in harmony with other people, take direction on each assignment, and give full attention to detail.

Data Science Internship

• Ensure quality and data reconciliation
• Attend and participate in sponsor meetings
• Manage all data management deliverables for assigned studies
• Keep supervisor informed of project status
• Review data and identifying errors/inconsistencies
• Collaborate with appropriate project team to resolve data issues
• Track outstanding issues and following-up until resolution
• Ensure accuracy in manipulation and scrutiny of clinical data

Clinical Data Manager

• Build and own the company's data function from the ground up
• Audit and improve existing data infrastructure, tooling, and processes
• Lead a lean data team while remaining hands-on with analysis
• Leverage AI to automate workflows and increase team productivity
• Analyze large datasets to answer strategic business questions
• Deliver investment-grade analyses, dashboards, and executive briefings
• Establish data governance, reporting standards, and analytical best practices
• Partner with Product, Finance, Operations, and Leadership to drive business decisions
• Continuously improve the company's AI-enabled data capabilities

Head of Data

• Delegating work and responsibility to subordinates
• Oversee scheduled shifts and evaluating the working of inventory staff and subordinates
• Generate monthly data from WMS reports indicating monthly and year-to-date totals of cycle count dollar variances
• Maintain a clean, neat, and orderly work area

Data Center Team Lead

• Own HR data management at Rakuten
• Manage the data governance framework for all Workday employee data
• Lead, develop, and retain a team of HR data management professionals
• Own the HR data workstream for recurring high-stakes programs
• Champion automation and AI adoption within HR Data Management
• Serve as a strategic partner to HR, Payroll, Global Total Rewards, Finance, Legal, IT, and HRIS

Manager, HR Data Management

• Work directly with the Division Analytics Chief Data Engineer, frontend data visualization specialists, and product stakeholders by developing, deploying, and maintaining data pipelines from authoritative data sources needed for metrics and analytics.
• Oversee both long-range goals and near-term objectives of the Division Analytics team related to collecting, managing, and converting raw data into usable information for business analysts to interpret and for division-wide accessibility.
• Provide mentorship and leadership to guide junior data engineers, conduct code reviews, and champion engineering best practices.

Staff Data Scientist

• Mining, modeling, and analyzing large datasets, utilizing predictive modeling techniques.
• Building and validating a variety of statistical models, providing analytic support, and developing new criteria and/or strategies.
• Design and implement experiments and processes for evaluating business performance, new products, and product features.
• Conducting required analyses incorporating project design, data collection, and analysis, summarizing findings, and presenting results in an understandable manner.
• Compiling appropriate data, applying multidimensional data aggregation, and performing profile analysis to evaluate business impact.
• Handling large volumes of transaction-level data to derive actionable results efficiently.
• Interacting with stakeholders to understand their business questions, crafting methodologies to mine/analyze datasets, and delivering insightful recommendations.
• Keeping up to date with the latest technology trends.

• Define the right problems: Before any analysis, understand what business question it's answering and what decision it will inform. Push back if the question is flawed.
• Data and insights: Define metrics, build reports, run analyses, design experiments, and surface data insights that move your team's key metrics.
• Drive outcomes: Bring recommendations to your counterparts in product, growth, finance or operations, tell them what's happening and why, and what next steps should be.

Associate Data Scientist

• Collaborate closely with Data Engineers to implement strategic engineering proof of concepts and contribute to technology strategy and engineering roadmaps
• Collaborate with clients and internal partners throughout the organization to identify and execute upon opportunities for demonstrating marketing data to drive business solutions
• Audit existing datasets for accuracy, reliability and consistency
• Advise senior team members to identify data models and algorithms to apply to unique client problems
• Increase and optimize user experiences, revenue generation, ad targeting and other business outcomes using predictive modeling and other advanced analysis techniques
• Coordinate with internal and client data engineering teams to implement models and supervise outcomes
• Support digital analytics implementations with broad understanding of tag management, HTML and JavaScript on websites
• Develop and test applications (visualization, routing data, cleaning data, access to data, interacting with data) for analytics pipelines
• Other duties as assigned

• Define reference architecture for agentic AI solutions
• Establish design patterns for Retrieval-Augmented Generation (RAG), Multi-agent orchestration
• Guide use-case prioritization and platform selection
• Build and deploy production-grade AI agents
• Implement: Prompt engineering, workflows, API integrations
• Establish enterprise AI guardrails, including Security and Data governance
• Configure and deploy Azure OpenAI / model endpoints
• Co-build pilot agents with consultants and internal teams
• Enable internal teams on agent design patterns and Responsible AI practices

Senior Data Scientist

• Manage the production process from concept through completion, ensuring creative quality while coordinating timelines, budgets, and resources
• Drive active data science projects to production — including a Next Best Action engine, customer segmentation model, and comp plan chatbot — defining what "done" looks like and holding the team accountable to shipping
• Establish a delivery cadence that balances exploration with execution, mentoring data scientists toward outcome-oriented habits: scoping work to what can be shipped, documenting what was built, and handing off cleanly
• Own the AI activation layer — ensuring data and models reach business users through practical surfaces, including Copilot, Fabric Agents, Power Automate flows, and Emarsys integrations
• Configure and optimize Microsoft Fabric semantic models for AI consumption and build MCP server integrations between Fabric and Claude (or equivalent) to make USANA's data accessible within internal AI tools
• Set technical direction for applied AI and ML — recommendation systems, customer segmentation, predictive churn, and intelligent automation — ensuring models are understood end-to-end from development through production handoff
• Partner with executive and commercial stakeholders to identify high-impact AI opportunities, translating complex technical concepts into actionable business strategies while championing responsible AI practices
• Collaborate cross-functionally with BI, marketing, data engineering, and IT teams to deliver end-to-end solutions built on trusted, well-governed data from USANA's Snowflake medallion architecture
• Define standards for model lifecycle management, feature engineering, and AI integration patterns — and stay current on the Microsoft AI ecosystem to evaluate emerging tools for strategic adoption

AI Activation, Data Science Manager

• 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 Scientist jobs in Utah

See all jobs on JobTailor

Senior AI Data Scientist

Director, Data Science

Data Scientist

Project Manager, Project & Development Services – Data Center

Product Manager – Data Enablement and Communications

Data Scientist

Data Science Internship

Clinical Data Manager

Head of Data

Data Center Team Lead

Manager, HR Data Management

Staff Data Scientist

Data Scientist

Associate Data Scientist

Data Scientist

Senior Data Scientist

AI Activation, Data Science Manager

Data & Analytics Lead