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

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Data Scientist II

• Develop and maintain strong relationships with clients, understanding their aftermarket needs and objectives.
• Act as the primary point of contact for key accounts, providing exceptional customer service and support.
• Conduct regular meetings with clients to review aftermarket performance, address concerns, and identify opportunities for improvement.
• Monitor and analyze the performance of data center aftermarket services and solutions provided to clients.
• Utilize data analytics and reporting tools to track key performance indicators (KPIs) and service levels.
• Identify trends, areas for improvement, and opportunities for optimization to enhance client satisfaction and operational efficiency.
• Address and resolve client issues or concerns promptly, coordinating with internal teams to ensure effective solutions.
• Manage escalations and work to resolve complex or critical problems that impact client performance or satisfaction.
• Develop and implement strategic account plans to align with clients’ business goals and requirements.
• Propose and execute initiatives to enhance service delivery, drive innovation, and support clients’ evolving needs.
• Prepare and present regular performance reports and reviews to clients, highlighting achievements, challenges, and future plans.
• Maintain accurate and up-to-date documentation related to account performance, service levels, and client interactions.
• Ensure that services and deliverables are provided in accordance with contractual agreements and service level agreements (SLAs).

Program Manager – Data Centers

• Lead and develop a team of data professionals, providing coaching, mentoring, and training to ensure high-quality project delivery and client satisfaction
• Oversee the processing and management of client pension data, including recurring data feeds and pension payroll file transmissions
• Direct data conversion activities for new client implementations and special projects, ensuring accurate data migration, validation, and integration
• Manage the delivery of annual actuarial valuation extracts, ad hoc reporting requests, data updates, and other client-specific processing needs
• Establish, maintain, and enforce data governance standards while identifying and implementing process improvements and system enhancements

Retirement Administration Data Manager – Defined Benefit

• 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

• Own retailer onboarding from kickoff through go-live.
• Manage weekly scan data submission health across the full retailer base.
• Serve as the primary point of contact for all retailer and CPG partner communications.
• Publish the weekly health dashboard for program performance.
• Coordinate with Scan Data Engineering to ensure technical setup and test submissions are completed.
• Monitor each submission cycle and communicate directly with retailers on action items.

Data Programs Manager

• Trusted customer master (the customer lighthouse): a single, governed customer master that includes IDN → member facility → service-line hierarchy, with GPO affiliation (primary and aggregation group), and identifiers integrated with market data
• Fixed, codified metric methodology that serves as one trusted source of truth: a documented, defensible calculation methodology for share-of-wallet, penetration, and addressable/potential value (built on market and GPO data)
• Redefined KPI framework: a smaller, value-based set of leading indicators that measurably changes where RVPs and reps spend their time and holds them accountable to execution
• Initiative tracking linked to EBITDA: a live tracking model where every transformation initiative is linked to its EBITDA impact (calculation owned by Finance) and rolls up to the North Star
• Phase 2 Lighthouse Expansion to the Product Lighthouse: a single, product master data methodology stood up on the same governed model
• Owns: Customer master data methodology and governance: hierarchy logic, GPO affiliation, identity-resolution rules, identifier crosswalks, and integration of market data into the customer lighthouse
• Calculation, methodology, and source of truth for derived commercial metrics (share-of-wallet, penetration, potential/addressable value)
• Design of Commercial KPIs including what is measured, how it is defined, and how it drives focus and accountability
• Initiative tracking model: tracking commercial transformation initiatives and linking each to its EBITDA impact
• The Definitive Healthcare (market-data) relationship, including managing scope changes within an approved budget
• Recommends: Where RVPs and reps should focus from data-driven insights to surface highest value whitespace and penetration opportunities from the trusted data for sales leadership and CVM to act on
• Data, pipeline, and tooling requirements to the ops-data organization focusing on the upstream demand that governs their build
• Sequencing of the lighthouse roadmap (customer → product) and the associated stewardship-ownership decisions
• Informs: Regional and rep-level performance transparency through the redefined KPI set
• Cross-functional and Finance leadership on data-quality gaps, unowned stewardship, and methodology changes that affect reported numbers

Commercial Analytics Lead

• Represent Supply Chain on the global platform management team, helping shape program scope to align with global sourcing strategies and preferred supplier partnerships.
• Lead end-to-end supply chain planning for new product launches, from concept development through production ramp-up.
• Partner with engineering and product teams to translate design requirements into scalable manufacturing and sourcing strategies.
• Drive cross-functional alignment across procurement, manufacturing, quality, and logistics to support launch readiness and execution.
• Optimize inventory requirements by analyzing customer forecasts, bills of material, and supplier lead times to support production needs.
• Drive material and supply chain readiness for New Product Introduction (NPI) builds, from concept phase through production handoff.
• Collaborate with sourcing leads to ensure program contracts and inventory stocking agreements support market lead-time requirements, program milestones, and production launch needs.
• Partner with Supplier Readiness and Supplier Development teams to ensure supplier capacity supports rapid Data Center growth.

Senior Manager, Supply Chain Program Manager – Data Centers

• Responsible for end-to-end delivery of data management services
• Collaborate with internal and external customers
• Ensure staff deliver high quality, timely service and meet all regulatory requirements
• Hire, manage staff performance, utilization, retention, process improvements
• Financial management for assigned projects

Manager, Clinical Data Management

• own multiple concurrent projects across model development, strategic analysis, and analytics product delivery — framing the business question, designing the approach, building the solution, and presenting findings to senior stakeholders (Lead Director, Executive Director, AVP)
• set technical direction for your workstream, mentor junior data scientists, and partner directly with business leaders across sales, underwriting, and finance to turn analysis into action

Senior Data Scientist

• Provide strategic and operational leadership for enterprise‑wide data science and artificial intelligence capabilities.
• Accountable for shaping and executing the Data & AI strategy.
• Translate business priorities into scalable analytics and AI solutions.
• Deliver measurable impact, accelerate innovation, and ensure responsible, compliant use of data and AI technologies.
• Define and lead the enterprise Data Science & AI strategy , roadmap and investment in alignment with business priorities and IT objectives.
• Build, lead, and develop high ‑ performing Data Science and AI team.
• Serve as a senior Technical voice for AI across the enterprise, translating business problems into AI opportunities and setting realistic expectations.
• Establish standards and reference architectures for generative and agentic AI.
• Partner with business and functional leaders to identify , prioritize, and deliver high ‑ value analytics and AI use cases.
• Design and operationalize the enterprise AI governance framework.

Senior Director, Data Science & AI, Orthopedics

• Define and lead the enterprise Data Science & AI strategy, roadmap and investment in alignment with business priorities and IT objectives, spanning machine learning, generative AI and agentic AI.
• Build, lead, and develop high ‑ performing Data Science and AI team.
• Serve as a senior Technical voice for AI across the enterprise, translating business problems into AI opportunities and setting realistic expectations grounding into what technology can and cannot do.
• Stand up and operate the enterprise AI hub; Shared platform, reusable patterns.
• Establish standards and reference architectures for generative and agentic AI, orchestration frameworks, retrieval patterns, semantic layers/knowledge graphs, agent action boundaries, observability and Human in the loop controls.
• Partner with business and functional leaders to identify, prioritize, and deliver high ‑ value analytics and AI use cases.
• Design and operationalize the enterprise AI governance framework; Model risk management, validation standards, explainability, bias and fairness testing, drift monitoring and audit trails.
• Track and evaluate emerging AI capabilities (frontier models, Agentic frameworks, domain specific models) and bring the right one into enterprise.

Senior Director, Data Science – AI, Orthopedics

• Own the product vision, strategy, and roadmap for data integration, analytics, and AI capabilities supporting vendor management optimization and cost transparency.
• Drive outcome based prioritization across initiatives (e.g., cost transparency, spend visibility, performance insights, workflow automation), balancing stakeholder needs, feasibility, and measurable impact.
• Translate business and operational needs into epics, user stories, acceptance criteria, and a prioritized backlog, partnering with delivery teams through refinement and release planning.
• Define and deliver product capabilities that enable vendor performance visibility, portfolio insights, and spend transparency, including clear success criteria and user outcomes.
• Define data requirements and integration needs, enabling reliable, analytics ready views across systems to support planning, forecasting, and performance tracking.
• Support delivery of AI ‑ assisted capabilities (e.g., forecasting, anomaly detection, insight summarization) within established platforms, standards, and guardrails.
• Lead ongoing backlog management, UAT, and release readiness, accountable for adoption and delivery of measurable business outcomes.
• Communicate product plans, trade-offs, risks, and insights clearly to business and technical stakeholders.
• Facilitate alignment across cross-functional partners with differing priorities through structured discussions and clear decision framing.
• Support governance, controls, and compliance requirements related to data, financial transparency, and operational processes.
• Help drive adoption of new product capabilities and workflows through clear documentation, enablement, and change support.
• Contribute to risk mitigation and continuous improvement through standards, transparency, KPI tracking, and post-implementation review.

Lead Product Manager, Data & AI Integration

• Translate customer needs into system features and manage their development
• Oversee the day-to-day management of Product Master Data products and services
• Shape the strategy for master data systems and processes within Merchandising Operations
• Collaborate with content, asset, and data governance stakeholders
• Create and maintain a prioritized backlog for product development
• Actively participate in Scrum events
• Develop, maintain and deliver a roadmap aligned with company initiatives

Master Data Product Manager

• 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

• Leveraging advanced analytics and statistical techniques to extract insights from large datasets
• Designing and developing robust data solutions to transform raw data into actionable insights
• Conducting exploratory and descriptive analysis to inform strategic decision-making
• Utilizing machine learning algorithms and predictive modeling to solve complex business problems
• Creating data visualizations to effectively communicate insights and drive data-driven decisions
• Collaborating with clients to understand their data needs and deliver tailored solutions
• Managing data pipelines and confirming data quality and integrity throughout the analysis process
• Applying data science workflows and troubleshooting techniques to optimize performance
• Engaging in continuous learning to deepen technical skills and adapt to emerging technologies
• Mentoring junior team members and fostering a collaborative team environment

• Leading data science initiatives to transform raw data into actionable insights for informed decision-making
• Guiding teams in the application of advanced analytics and statistical techniques to solve complex business problems
• Developing and implementing data models and algorithms to enhance predictive capabilities
• Overseeing the creation and maintenance of data pipelines and data lakes to support large-scale data processing
• Utilizing machine learning and deep learning techniques to drive innovation and improve data-driven strategies
• Collaborating with stakeholders to identify opportunities for leveraging data to drive business growth
• Coaching team members to enhance their skills in data science workflows and troubleshooting
• Validating data quality and integrity within analytics frameworks to support reliable insights
• Encouraging the adoption of innovative technologies and methodologies in data science projects
• Managing project timelines and deliverables to align with client expectations and business objectives

AI Data Scientist Manager

• Define and own the strategic vision and roadmap for data products across multiple business domains.
• Translate high-level business problems into technically grounded data product requirements rooted in consumption-ready data architectures, Kimball-inspired dimensional modeling concepts, and Domain-Driven Design (DDD) bounded contexts.
• Align roadmap priorities with overall company objectives and communicate strategic direction to stakeholders at all levels.
• Lead requirements discovery and translate business needs into precise, engineer-ready specifications.
• Author acceptance criteria that bridge business intent and technical implementation.
• Partner directly with Data Product Engineers and Data Architects to bridge business intent and technical execution.
• Collaborate with AI/ML and data science teams to define data product requirements that support machine learning initiatives.
• Define and enforce data governance standards, metadata management practices, data lineage tracking, and data observability frameworks across the data product portfolio.
• Partner with Data Governance and Architecture teams to ensure all data products meet quality, security, and compliance requirements.
• Define KPIs and success metrics, monitor product performance, and use feedback to guide continuous iteration.

Lead Data Product Manager

• Provide independent data science, machine learning, and analytical insights using member, financial, and organizational data to support mission critical decision making for various areas of the organization.
• Understand business needs and identify opportunities for new products, services, and process optimization to meet business objectives through the use of cutting-edge data science.
• Conduct work assignments of increasing complexity, under moderate supervision with some latitude for independent judgment.
• Evaluate moderately complex predictive models and advanced algorithms.
• Identify meaningful insights from large data and metadata sources.
• Test hypotheses/models, analyze, interpret, and document results.
• Review input data, assumptions, and outcomes of models and analytical frameworks to assess potential risk and fairness considerations. Exercise sound judgment and discretion within defined procedures and practices.
• Develop and code moderately complex software programs, algorithms, and automated processes.
• Use modeling and trend analysis to analyze data and provide insights.
• Develop understanding of best practices and ethical AI.
• Transform data into charts, tables, or formats that aid effective decision making.
• Build working relationships with team members, business partners, compliance, legal, and subject matter experts.
• Lead small projects and initiatives.
• Utilize effective written and verbal communication to document and present findings of analyses to a diverse audience of stakeholders.

• 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

Data Scientist jobs in Florida

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Senior AI Data Scientist

Data Scientist II

Program Manager – Data Centers

Retirement Administration Data Manager – Defined Benefit

Data Scientist

Data Programs Manager

Commercial Analytics Lead

Senior Manager, Supply Chain Program Manager – Data Centers

Manager, Clinical Data Management

Senior Data Scientist

Senior Director, Data Science & AI, Orthopedics

Senior Director, Data Science – AI, Orthopedics

Lead Product Manager, Data & AI Integration

Master Data Product Manager

Clinical Data Manager

Senior AI Data Scientist

AI Data Scientist Manager

Lead Data Product Manager

Senior Data Scientist

Head of Data