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 the technical delivery both as a project manager and site lead for mission critical commissioning projects with minimal supervision while completing assignments that require independent judgement and evaluation.
• Maintain effective communication with clients, contractors and other project team members.
• Develop and maintain client relationships.
• Manage project specific commissioning plans, commissioning procedures, checklists, test scripts, and schedules for Level 1 through Level 5 commissioning activities.
• Lead onsite teams to perform functional performance tests on mechanical, plumbing, and electrical systems.
• Provide project management and coordination to meet client expectations for technical excellence, budget and schedule.

Data Center Commissioning Project Manager

• NLP & Utterance Analysis: Leverage Natural Language Processing (NLP) and machine learning to categorize and cluster raw user utterances. Perform sentiment analysis on unstructured text logs to extract actionable product insights.
• AI Response Evaluation & Experimentation: Design methodologies to evaluate the helpfulness, accuracy, and relevance of the AI’s responses. Design and analyze A/B tests to measure the impact of prompt adjustments, model updates, and new feature rollouts.
• Data Integration & Sanitization: Dive directly into Google Cloud Platform (GCP) to cleanly join and structure mobile, customer support, and vehicle data into robust "Analytical Sandboxes," ensuring strict adherence to data privacy and PII handling standards.
• Problem Framing & Metric Definition: Act as a strategic partner to Product Managers. Challenge assumptions and define core conversational metrics (e.g., task success rates, user engagement, support deflection).
• Advanced Visualization & Self-Service: Design, build, and maintain highly intuitive, narrative-driven dashboards using Looker and PowerBI to empower the product team to answer their own day-to-day questions.
• Bridge the Mobile-to-IVI Gap: Act as the analytical bridge as our digital assistant expands from the Ford app into the vehicle, standardizing mobile data against our emerging in-vehicle data contracts.

Data Scientist – Conversational AI

• Integrated Visibility: Maintain a single, unified view across Data Migration, ODPS/Semantic Layer, Data Warehouse, Reporting, downstream applications, and UAT to ensure end-to-end lifecycle alignment.
• Cross-Team Facilitation: Drive collaboration from requirements through validation, ensuring data mapping has full team participation, progress tracking, and a formal change process for reopening completed mappings.
• Sequencing & Coordination: Manage sequencing across data mapping, availability, warehouse consumption, report builds, and downstream application testing while aligning with other key initiatives' milestones and impacts.
• Risk/Issue Mitigation: Proactively identify gaps, conflicts, and risks, facilitating resolution before they threaten milestones or deliverables. Maintain an integrated log of risks, issues, assumptions, decisions, and dependencies, driving resolution of cross-workstream issues.
• Integrated Planning: Build and maintain schedules, dependency maps, and readiness plans spanning Product, Engineering, Data, Reporting, Testing, and Transformation Office teams.
• Critical Path Management: Track critical path activities, escalating concerns with clear, fact-based options and recommendations.
• Release Support: Support PI, release, and go-live planning wherever data, reporting, or downstream applications sit on the critical path.
• Decision Facilitation: Lead decision-making forums on complex delivery topics, ensuring outcomes are documented and actionable, and escalating unresolved items through governance forums.
• Traceability: Maintain end-to-end traceability from requirements through mapping, semantic layer exposure, warehouse consumption, reporting, and testing.
• Execution Transparency: Drive Jira hygiene, dependency tracking, and dashboard reporting to ensure accurate status and readiness data across teams.
• Leadership Reporting: Prepare concise updates on status, risks, issues, decisions, and readiness health for leadership.
• Readiness Tracking: Monitor readiness of critical data assets, reporting capabilities, and validation activities, partnering on UAT planning and issue resolution.
• Launch Alignment: Connect reporting readiness to broader launch, training, and go/no-go planning, giving leadership clear visibility into launch preparedness.
• Success Outcomes: Ensure integrated visibility, managed dependencies, full traceability, and that data/reporting workstreams meet program commitments and launch readiness expectations.

Data & Reporting Program Integration Manager

• Developing industry leading analytics solutions
• Assessing existing analytic models and architectures, reviewing for gaps against business needs and industry best practices
• Identifying detailed data needs, sources, and structure to support solution development
• Determining the analytic approaches to be used and defining and applying the appropriate methodologies
• Employing Proof-of-Concepts for exploratory and targeted data analyses, leading to breakthrough solutions
• Ensuring integrity of the analytical solutions in terms of the underlying statistical and economic models and assumptions
• Designing and developing project-specific models.
• Architecting analytical tools to productionize cloud-based analytical models
• Monitoring and sustaining model effectiveness

Staff 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

• Own the process for prognostic feature development from conceptual to feature deployment to our production vehicles.
• Pioneer Physics-Informed Machine Learning (PIML): Fuse first-principles physics modeling with advanced machine learning to develop hybrid, high-fidelity prognostic models that capture complex degradation behaviors across both EV and ICE powertrains.
• Architect Prognostics & RUL Frameworks: Design and deploy state-of-the-art prognostics models to accurately estimate the Remaining Useful Life (RUL) of critical vehicle subsystems, transforming noisy fleet data into actionable maintenance alerts.
• Deploy Edge Models in C++: Translate complex predictive models into highly optimized, low-latency C++ code, bridging the gap between cloud-based data science and resource-constrained on-board vehicle electronic control units (ECUs).
• Harness High-Frequency Signal Processing: Architect custom Digital Signal Processing (DSP) pipelines and time-series analytics to extract clean, high-frequency physical signatures from multi-sensor vehicle networks, isolating early-stage wear patterns before they manifest as failures.
• Design Multi-Sensor Fault Detection & Isolation (FDI): Develop and validate intelligent, multi-sensor anomaly detection frameworks capable of real-time Fault Detection and Isolation (FDI) to ensure vehicle safety, system redundancy, and fault-tolerant control.
• Apply Statistical Causal Inference: Leverage advanced statistical methods (including causal inference, multivariate analysis, ANOVA, and PCA) to differentiate between mere correlation and true physical root causes of component degradation across massive, connected vehicle fleets.
• Own the End-to-End Pipeline (HIL to Production): Direct the entire prognostic lifecycle—moving seamlessly from mathematical conceptualization and simulation in MATLAB/Simulink to physical validation on Hardware-in-the-Loop (HIL) benches, prototype vehicles, and ultimately to production vehicle deployment.
• Synthesize Deep Subsystem Domain Knowledge: Partner closely with EV and ICE component subject matter experts to translate deep physical domain knowledge (thermal, mechanical, chemical, and electrical) into robust on-board and off-board diagnostics.
• Build Scale with Big Data & Calibration Tools: Ingest and process large-scale telemetry data using Python, SQL, Spark, and Hadoop, while leveraging industry-standard calibration tools (such as ATI and ETAS) to fine-tune algorithms for real-world driving environments. Interact with subject matter experts to understand component/system functions, leverage existing connected vehicle data to model on-board and off-board prognostics algorithms.
• Operate cross-functionally to ensure successful code implementation on production vehicles.

Applied Data Scientist – Vehicle Prognostics

• 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

• Lead the development of the Indirect Program Management Process, leveraging advanced data analytics and Power BI to build robust dashboards that communicate the health, spend, and milestones of indirect programs to top-level leadership.
• Drive project management for key initiatives and serve as an indirect program buyer when required—ensuring the on-time sourcing of indirect goods and services.
• Shape the Future of Procurement: You will be at the forefront of transforming our sourcing practices, directly contributing to operational excellence, cost savings, and waste elimination.
• Dual-Role Growth: Expand both your technical/analytical capabilities and your hands-on commercial procurement skills, making you a highly versatile leader within the Supply Chain organization.
• High-Level Visibility: Your Power BI dashboards and strategic project updates will directly inform and guide decisions made by global purchasing and plant leadership.

Indirect Procurement Program Strategy – Analytics Lead

• Lead the continuous improvement of complex predictive models, leveraging advanced statistical and machine learning methodologies to ensure state-of-the-art accuracy.
• Drive the transition of analytical frameworks into robust, production-ready assets, focusing on deployment automation, code efficiency, and operational stability.
• Manage the end-to-end model lifecycle, including performing regular scheduled updates and executing rapid-response ad-hoc analyses to support strategic decision-making.
• Proactively assess existing toolsets and methodologies to identify opportunities for enhancements and innovations.
• Translate intricate quantitative concepts into clear, actionable insights for both technical peers and non-technical stakeholders.
• Partner closely with cross-functional teams to align data science capabilities with evolving business needs and maximize impact.
• Present findings, recommendations, and progress updates to senior leadership and other key stakeholders.

Senior Specialist, Yield Management – GTM AA Data Scientist

• Design, develop, and deploy ML solutions ranging from traditional ML applications (classification, clustering, recommendations) to LLM-based systems, including document parsing, data extraction, RAG pipelines, and LLM agents
• Write clean, maintainable, production-quality Python code that integrates smoothly with existing engineering and deployment infrastructure
• Work with large datasets to clean, preprocess, and analyze data, ensuring data quality and integrity
• Implement and optimize algorithms using best practices in machine learning, deep learning, and statistical analysis
• Collaborate with business stakeholders to understand requirements and deliver data-driven solutions that provide actionable insights
• Develop and maintain scalable pipelines and infrastructure for data processing and model training, versioning, deployment, and monitoring
• Evaluate the performance of machine learning models, including LLM-specific evaluation approaches, and tune models for optimal performance
• Communicate findings, insights, and model performance to both technical and non-technical audiences
• Continuously stay updated on the latest trends, technologies, and best practices

Machine Learning Engineer, Data Scientist

• As a Senior Data Scientist, you will use your knowledge of data and advanced analytics to identify and articulate the role data and analytics products play in helping the business achieve their goals.
• You will collaborate with Data Engineers and Software Engineers to develop robust analytics products.
• You will collaborate with partners in purchasing, product development, manufacturing, warranty, material planning, logistics and other Ford functions to define problems, identify data, develop data pipelines, develop metrics, develop analytics products using your expertise in visualization, AI/ML, Statistics and Optimization, create machine learning models, leverage operations research techniques, and deploy software solutions to provide actionable insights that deliver measurable via Google Cloud Platform to optimize the delivery of value.
• Designing, training, and fine-tuning AI models (including Deep learning and LLMs) to solve specific business problems.
• Familiar with LLM orchestration workflows like Langraph, Google ADK for quick development and scaling strategies to build robust pipelines.
• Familiar with AI Graph DB platform, use native graph query and conduct LLM extraction into actionable insights.
• Transitioning models from research environments to production, often by converting them into APIs or integrating them into existing software applications.
• Building and maintaining the infrastructure for AI development, data pipelines, and automated workflows.
• Working with data scientists to define AI strategies, understand requirements, and implement solutions.
• Testing, validating and monitoring AI models in production scale to ensure reliability.
• Staying current with AI advancements (e.g., Generative AI, LLMs) and applying them to improve existing products.
• Accelerate the application of value-added analytics and machine learning into the portfolio of products for supplier risk team.
• Drive analytic excellence into product teams by collaborating with Data Scientists, Data Engineers and Software Engineers in analytic and machine learning methods.
• Work closely with the Product Manager and Product Owner to translate Business Value needs into analytic deliverables and, where appropriate, software products for delivery by product teams.
• Act as a consultant to the business vs. an order taker.

Senior Data Scientist – AI Specialist

• Guide, mentor, and support a team of 4–6 AI/ML engineers and data scientists, overseeing daily standups, sprint planning, and code reviews.
• Lead the hands-on development, testing, and deployment of predictive models, time-series forecasting, and optimization algorithms.
• Directly oversee the development, maintenance, and refinement of demand sensing algorithms to support sales and operations planning (S&OP) and optimize yield.
• Drive the practical integration and deployment of Agentic AI solutions to automate complex workflows and operational processes.
• Ensure the team builds robust, automated pipelines for data ingestion, model training, deployment, monitoring, and retraining in production environments.
• Establish and enforce best practices for clean code, model versioning, comprehensive documentation, and system scalability.
• Maintain rigorous standards for data integrity and model validation, ensuring all solutions adhere to ethical and responsible AI guidelines.
• Partner with product managers, software engineers, and business analysts to translate functional requirements into technical specifications.
• Manage project timelines, unblock technical hurdles, and regularly communicate project status and technical milestones to the Senior Manager.

Manager, Yield Management – GTM AA Data Scientist

• Model Development & Analytics: Design, train, and evaluate machine learning models, including predictive, classification, and ensemble methods, and conduct exploratory data analysis to surface trends, anomalies, and decision-support signals
• AI Application & LLM Integration: Build and integrate LLM powered workflows for insight generation and decision support, blending structured business metrics with external signals through effective prompt engineering and harness in the agent
• Data Pipeline & Engineering: Design, build, and maintain scalable ETL and data pipelines across multi-source datasets to power analytics, reporting, and downstream applications
• Data Products & Visualization: Develop interactive analytics applications and dashboards (such as Dash/Power BI) that deliver real-time analytics, KPI monitoring, and actionable business insights
• Model Evaluation & Data Quality: Establish model evaluation frameworks grounded in statistical metrics and business KPIs, and safeguard data reliability through validation of completeness, consistency, and ongoing pipeline monitoring
• Collaboration & Delivery: Partner with data engineers, software engineers, and product owners to translate business needs into robust analytic deliverables, balancing technical rigor with speed to delivery

Yield Management Specialist – GTM, Data Scientist

• Supervise a global cross-functional team composed of data scientists, data analysts, data engineers, product designers, program managers, and software engineers.
• Provide leadership, mentorship, and guidance to ensure effective collaboration and successful delivery of analytics-driven solutions and product initiatives.
• Foster an environment of open communication and teamwork, facilitating alignment across diverse technical and business disciplines to achieve organizational goals and continuous improvement.
• Work closely with stakeholders throughout Ford Operations/Enterprise to identify opportunities to improve process optimization and efficiency via data-driven approaches.
• Lead, coordinate, and communicate work across multiple teams.
• Organize large datasets and get actionable insights for business.
• Apply data mining, data transformation, and develop statistical, optimization, and machine learning models.
• Create meaningful data visualization, outputs files and presentations to communicate findings with stakeholders.
• Work with IT to implement analytics tools into a production environment.
• Develop analytic models using skills such as data wrangling, algorithm design, prototype and deployment.
• Support cross-function teams on Reductive Design decisions with insights gained from analytics tools and visualization from the data.
• Problem formulation for Reductive Design problems.
• Acquire a deep understanding of business problems, define the problem scope from business requests and translate business requirements to analytical projects through Design Thinking.
• Work effectively with a wide range of stakeholders and functional teams.
• Interpret results and communicate them to technical and non-technical audiences, cross functional teams, and executive leadership.
• Support ad hoc requests and serve as insight provider/consultant for business units.
• Have ownership of the project, create a project plan, track project progress, and host user group meetings.
• Run efficient SQL to process TB-level datasets.
• Design a visual interface for users to interact with the data, or persuading visualization.
• Provide training and maintenance of implemented tools to stakeholders.
• Build advanced analytics models in the supply chain space.

Product Manager – Data Science Supervisor

• 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

• Design analytical solutions that harness internal and external data, leveraging visualizations, predictive analytics, and prescriptive methods.
• Collaborate with partners in purchasing, product development, manufacturing, warranty, material planning, and logistics to define problems, identify data, and develop machine learning algorithms.
• Evaluate large, complex data sets using SQL, Hadoop, Alteryx, Python, R, and sophisticated statistical and machine learning techniques.
• Develop business solutions using data acquisition, data engineering, programming, and visualization skills, designing intuitive user interfaces.
• Ensure the overall quality of data solutions throughout the analytic development process.

Data Scientist – Supply Chain Analytics

• Develop and deploy advanced analytics frameworks to identify anomalous vehicle behaviors, diagnostic trends, and emerging quality risks across millions of connected vehicles.
• Apply statistical methods, machine learning, and AI-driven detection techniques to improve early warning capabilities and reduce time-to-detection for vehicle quality issues.
• Design scalable algorithms and data models capable of analyzing billions of telemetry and diagnostic data points across vehicle platforms.
• Lead enterprise use of connected vehicle data throughout the product lifecycle, including pre-production validation, launch monitoring, and in-market quality surveillance.
• Define strategies for leveraging cloud-based telemetry, diagnostic, and software interaction data to improve product quality outcomes.
• Drive continuous improvement of detection logic, escalation frameworks, and data-driven quality processes.
• Lead use of connected data for quality across the entire product lifecycle, from TT builds in CVLQ to MP1 builds in EWRT and post-OKTB data in UCS.
• Lead deep-dive investigations into software, hardware, calibration, and systems integration issues using connected vehicle data.
• Correlate signals across vehicle functional domains to identify root causes and systemic quality trends.
• Partner with Product Development, Software, Hardware, Manufacturing, and Quality teams to drive issue resolution and containment actions.
• Drive development of automated analytics pipelines, AI-enabled monitoring tools, and executive dashboards supporting enterprise quality initiatives.
• Partner with data engineering and platform teams to improve data accessibility, reliability, and scalability across cloud environments.
• Champion modern data science methodologies and operational analytics best practices within the organization.
• Lead cross-functional initiatives focused on launch quality improvement, issue prevention, and reduction of customer-impacting failures.
• Translate highly technical findings into concise executive-level insights and strategic recommendations.
• Influence decision-making across engineering, quality, and leadership organizations without direct authority.

Vehicle Connected Data Manager

• Design, develop, validate, and deploy machine learning and AI solutions for business-critical applications.
• Build scalable predictive models, anomaly detection systems, forecasting solutions, recommendation systems, and generative AI applications.
• Develop conversational AI and agent-assist solutions leveraging LLMs, NLP, and retrieval-augmented generation (RAG) techniques.
• Create intelligent AI agents for business workflow automation and SDLC acceleration initiatives.
• Develop and optimize fraud detection models using supervised and unsupervised machine learning techniques.
• Analyze structured and unstructured datasets to identify trends, patterns, risks, and business opportunities.
• Partner with engineering teams to productionize AI/ML solutions and integrate them into enterprise applications and workflows.
• Develop reusable ML pipelines, feature engineering frameworks, and model monitoring capabilities.
• Monitor model performance, drift, reliability, and operational effectiveness in production environments.
• Collaborate with product managers, engineers, business stakeholders, and risk/compliance teams to define requirements, success metrics, and implementation strategies.
• Translate technical insights and analytical findings into clear business recommendations and executive-level communications.
• Ensure AI and machine learning solutions comply with data governance, privacy, security, and regulatory standards.
• Develop documentation supporting model explainability, validation, monitoring, and audit readiness.
• Promote responsible AI practices, including fairness, transparency, and risk mitigation.
• Mentor junior team members and contribute to technical standards, best practices, and continuous improvement initiatives.

Senior Data Scientist

• Develop and deliver high-impact analyses, dashboards, and reports to track marketing performance, campaign ROI, funnel metrics, and customer behavior
• Build intuitive, self-serve visualizations using tools like Power BI to support marketers in tracking their KPIs
• Develop and maintain scalable data models, ensuring data integrity and accuracy in reporting
• Serve as a trusted analytics advisor to marketing leaders and channel owners, providing data-driven recommendations to optimize campaigns and programs
• Contribute to the development of measurement frameworks, attribution models, and marketing mix optimization
• Lead/participate in cross-functional collaboration with data engineering, digital, and product teams to ensure analytics solutions are scalable and aligned with business needs

Data Scientist jobs in Missouri

See all jobs on JobTailor

Senior AI Data Scientist

Data Center Commissioning Project Manager

Data Scientist – Conversational AI

Data & Reporting Program Integration Manager

Staff Data Scientist

Data Scientist

Applied Data Scientist – Vehicle Prognostics

Data Strategy and Governance Manager

Indirect Procurement Program Strategy – Analytics Lead

Senior Specialist, Yield Management – GTM AA Data Scientist

Machine Learning Engineer, Data Scientist

Senior Data Scientist – AI Specialist

Manager, Yield Management – GTM AA Data Scientist

Yield Management Specialist – GTM, Data Scientist

Product Manager – Data Science Supervisor

Data Scientist

Data Scientist – Supply Chain Analytics

Vehicle Connected Data Manager

Senior Data Scientist

Marketing Analytics Lead