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

• Responsible for full lifecycle program management in hyperscaler cloud data center projects
• Lead cross-functional teams to ensure timely and successful delivery of key projects
• Engage with stakeholders to gather requirements and provide updates
• Analyze project performance and report on key metrics
• Mentor junior team members and foster a collaborative environment

Senior Principal Program Manager – Hyperscaler Cloud Data Center, Full Lifecycle Program Ownership

• 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

• Lead, coach, and develop the Salesforce Performance Analytics team
• Drive the strategy and evolution of analytics, forecasting, insights, and AI-enabled capabilities
• Shape a product and outcome-oriented roadmap aligned to business and Field priorities
• Bring an enterprise perspective to analytics decisions while staying closely connected to Field needs
• Translate business needs into scalable analytics solutions that improve decision-making and performance
• Set team priorities across support, enhancements, and strategic capability-building work
• Oversee service tickets, configuration changes, and core data load activities
• Drive continuous improvement in platform usability, operational consistency, and team effectiveness
• Partner cross-functionally to support platform changes, integration readiness, and adoption
• Maintain accountability for platform stability, operational health, and administrator-owned processes

Lead Director, Performance Analytics

• Lead end-to-end data center campaign strategy, execution, and performance measurement across global markets
• Translate corporate narratives into clear campaign objectives, messaging, content, and activation plans
• Orchestrate cross-functional teams and agency partners to ensure consistent messaging and on-time delivery
• Develop audience-focused content aligned to industry trends, use cases, and product proof points
• Monitor campaign performance, drive optimization, and deliver insights for continuous improvement

Staff Data Center Campaign Lead

• Advise clients on end-to-end solutions across data strategy, data management, and data quality.
• Support business development, methodology development, team leadership, and delivery execution for data governance assessments and implementations.
• Design and develop data governance technology solutions across data quality, data dictionaries, business glossaries, data standards, and data policies.
• Define organizational design, governance processes, policies, roles, responsibilities, success metrics, and operating models for client data programs.
• Build roadmaps covering technology, culture, governance, processes, skills, competencies, data quality, analytics, and data management maturity.
• Lead and collaborate with data governance teams to create business cases, resource plans, implementation roadmaps, and enterprise data system assessments.
• Assess current-state environments, identify client pain points and requirements, and help define a practical vision and strategy for becoming a data-driven organization.
• Advise clients as a trusted partner and provide thought leadership on data governance, data management, cloud transformation, AI readiness, and related best practices.
• Manage resources, budgets, project delivery, team development, training, mentoring, and performance assessment.

Data Strategy and Governance Manager

• Collaborate and communicate with all levels of management to provide reporting and critical information.
• Creates analysis and visualization materials for business data, metrics, and measures.
• Designs, validates, and schedules queries and reports in various databases and reporting environments.
• Works closely with end users and management to evolve, improve, and automate existing data processes.
• Develops specifications, documentation, and end user materials.
• Documents and maps existing and desired business processes.
• Deploys reports, SQL database objects, and applications to on-premises and cloud environments.

Enterprise Data Lead Analyst

• Develop, implement, and deploy machine learning models and statistical analyses to optimize energy storage and solar asset performance across Stem's portfolio
• Design and build data pipelines to extract, transform, and analyze large-scale datasets from energy systems
• Collaborate with software engineering, product management, and energy experts to integrate data-driven solutions into PowerTrack platforms
• Create data visualizations, dashboards, and reports to communicate insights and recommendations to stakeholders
• Conduct exploratory data analysis to identify trends, patterns, and opportunities for improving system performance and business outcomes

Data Scientist, Forecasting – Energy Storage System Optimization

• Partner with Marketing to Define and Solve Problems – Work closely with marketing stakeholders to understand business challenges, define success metrics, and translate needs into analytical approaches that drive performance across campaigns and channels.
• Design and Deliver Data-Driven Solutions – Apply statistical analysis and machine learning to develop solutions that address business needs, then present findings, influence decisions, and gain alignment on adoption.
• Lead Experimentation and Optimization – Develop and manage testing frameworks (A/B testing, campaign experimentation) across channels and markets. Analyze results and provide clear recommendations to improve performance and inform future strategy.
• Translate Results into Business Impact – Clearly communicate insights and quantify outcomes (e.g., campaign performance lift, engagement improvements, ROI) to ensure stakeholders understand the value and take action.
• Partner to Operationalize Solutions – Collaborate with Technology and Engineering teams to transition validated models into production, supporting implementation through scalable pipelines and processes.
• Influence Through Storytelling – Present insights and recommendations to both technical and non-technical audiences, including senior stakeholders, to drive alignment and decision-making.

Senior Data Scientist – Marketing Analytics

• Lead the development of predictive models for finance use cases
• Build and implement scalable, production-grade analytical models that integrate with enterprise finance systems and data platforms
• Develop, test, and deploy intelligent agents in a Snowflake environment
• Partner closely with Finance business stakeholders, Data Engineering, and UX teams
• Provide technical leadership and mentorship to junior data scientists

Staff Data Scientist

• Leads, develops, and grows a high-performing team of data scientists and machine learning engineers focused on clinical analytics, risk adjustment, and AI/ML model development; sets technical direction, mentors team members, and builds bench strength across statistical modeling, machine learning, and clinical domain expertise.
• Oversees the design, development, and deployment of advanced predictive models, machine learning algorithms, and AI solutions that evaluate clinical scenarios, forecast member risk and outcomes, and identify opportunities to improve quality of care and risk score accuracy.
• Directs data science initiatives leveraging data mining, statistical modeling, natural language processing, and machine learning techniques across clinical, claims, pharmacy, lab, and member-reported data sources.
• Establishes the team's technical standards and best practices for model development, validation, documentation, monitoring, and retraining, ensuring models are explainable, reproducible, and aligned with regulatory and organizational requirements.
• Applies deep expertise in clinical analytics and risk modeling (e.g., HCC/CMS-HCC, condition prevalence, cost and utilization prediction, readmission risk, care gap identification) to deliver models that drive measurable business and clinical impact.
• Develops analytical frameworks, key metrics, and performance dashboards to monitor model performance, clinical quality, risk adjustment outcomes, and operational efficiency.
• Partners with executives, clinicians, actuarial, finance, IT, and business stakeholders to understand priorities, frame analytical problems, and deliver data-driven solutions that inform enterprise strategy.
• Delivers presentations and consultative briefings to senior leadership and cross-functional partners, translating complex modeling concepts and results into clear, actionable recommendations.
• Communicates with leaders to introduce and operationalize incremental KPIs and modeling capabilities that contribute to revenue accuracy, medical cost management, and quality performance targets.
• Establishes and enforces adherence to data governance, model risk management, privacy, and compliance policies, and educates team members on data science and modeling best practices.

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