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 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

• 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

• Establishing trust with key stakeholders to secure long-term partnerships.
• Understanding client needs to propose solutions that ensure their success and expand account value.
• Serving as the key link between clients and internal teams (marketing, sales, support) to meet deadlines.
• Negotiating contracts, managing budgets, and preparing regular reports on account progress.
• Acting as the first point of escalation to solve issues and maintain satisfaction.
• Holding meetings with c-level executives externally and internally.

National Account Manager, Data Center Colocation

• Support deployment and sustainment of demand forecasting & inventory planning software tool
• Simulate complex data models using planning tool to sustain forecasting and inventory optimization activities
• Work with supply chain experts and technical leaders

Senior 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

• 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

• Extract, prepare and model large, complex data sets using a combination of skills, including machine learning theory, mathematics, statistics, and programming.
• Deliver on-time quality analysis, interpretation, and synthesis of data into effective, concise, and actionable recommendations that enable intelligent decisioning for the company.
• Provide senior-level guidance and mentorship to the data science team, including reviewing projects, models, and code for peers and junior team members.
• Work with engineering teams to implement and improve machine learning pipelines and production-ready models.
• Effectively communicate important information and insights to business leaders using verbal, written, and data visualization skills.
• Also responsible for other Duties/Projects as assigned by business management as needed.

• 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.

Data Scientist jobs in Kansas

See all jobs on JobTailor

Senior AI Data Scientist

Data Scientist

Data Strategy and Governance Manager

National Account Manager, Data Center Colocation

Senior Data Scientist

Data Scientist

Data Scientist, Forecasting – Energy Storage System Optimization

Senior Data Scientist

Data & Analytics Lead

Senior Director, Clinical Data Science