Top Senior Data Science Jobs in Kansas City, MO

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Build and deploy machine learning models for credit and fraud risk, using large-scale financial data to improve lending decisions and business performance. Monitor production models, validate algorithms and features, conduct risk analyses, follow regulatory and responsible AI practices, and collaborate with engineers and cross-functional stakeholders. Develop productivity tools, document models, and communicate technical findings to senior management.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Conduct workforce analytics and applied research on attrition, engagement, mobility, performance, and planning. Perform statistical analyses, regression modeling, data extraction and validation using SQL and R or Python. Translate HR business questions into actionable insights, communicate findings to stakeholders, and protect sensitive workforce data.

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Own the product modeling roadmap and end-to-end lifecycle of production machine learning models, including recommenders, routing, and agent assignment. Engineer features, design experiments, deploy iterative improvements, and define real-time model integration with engineering and product teams. Apply Bayesian methods, causal inference, and marketing measurement while translating uncertainty into business recommendations. Mentor teammates and raise data science standards through code review and documentation.

Develop and deploy statistical and machine learning models for credit risk, delinquency, defaults, loan losses, underwriting, and risk-based pricing. Analyze credit bureau, alternative, and loan performance data; build production-ready analytical pipelines; monitor model performance; and address data anomalies. Lead complex initiatives, mentor colleagues, document methodologies, and incorporate model governance, fair lending, privacy, and responsible AI considerations while partnering with Credit Risk, Product, Engineering, and Finance.

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Analyze modeled outputs and research data to produce automated reporting, visualizations, statistical insights, and actionable guidance for political strategists and clients. Build analytics pipelines, conduct regression-based analysis, monitor research returns, identify data quality issues, and collaborate with engineers, machine learning scientists, statisticians, and political experts.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Develop data pipelines, organize and analyze structured and unstructured government data, create visualizations, build AI agents and lightweight automations, and occasionally develop predictive models. The role involves consulting with senior stakeholders, communicating actionable insights, supporting data capacity building, and collaborating with product, design, engineering, and research teams in a remote government-services environment.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

The Data Scientist will transform real-world, clinical, and preclinical healthcare data into reproducible datasets, analyses, and evidence supporting medical-device research and regulatory needs. Responsibilities include cohort development, data-quality validation, statistical programming, study design support, analysis plans, clinical and post-market analyses, documentation, and communicating findings to technical and nontechnical stakeholders. The role collaborates with Scientific, Clinical, R&D, Regulatory, Quality, IT, and external data partners.

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

Develops statistical, machine learning, AI, and predictive solutions to solve complex business problems. Mines and analyzes large datasets, builds algorithms and automated data processes, validates models, and communicates insights to business and product leaders. Designs enterprise AI applications, including generative AI and RAG solutions, while supporting data governance, visualization, security, scalability, monitoring, and responsible AI practices.

Build and improve machine learning models that identify healthcare claims errors and fraud for audit. Responsibilities include curating labeled data, engineering features, selecting optimization metrics, measuring model and business impact, refining large-scale data pipelines, and developing modeling infrastructure and team tools. The role also involves collaborating with clients and healthcare experts to align insights with business needs.

Build and deploy production machine-learning models for member identification, personalization, provider search, and healthcare AI tools. Lead projects from problem framing and prototyping through product handoff, design experimentation and causal analysis, define product metrics, and develop reliable feature pipelines and experiment infrastructure. Partner with product, engineering, care operations, marketing, and analytics engineering while communicating recommendations clearly and ensuring models are monitored, tested, and impactful.

Own marketing data science strategy, including campaign experimentation, causal inference, multi-touch attribution, monitoring, and predictive models for LTV, drop-off, segmentation, propensity, and personalization. Build reusable data models and semantic-layer assets, ensure analytical rigor, and partner with marketing and data teams to guide campaign decisions. Work remotely with occasional travel.

Leads product analytics strategy across Xbox experiences, establishing metrics, experimentation, causal inference, forecasting, and machine-learning approaches to guide investments and improve growth outcomes. Partners with senior Product, Engineering, and Business leaders, builds scalable measurement frameworks and AI-enabled analytical capabilities, strengthens telemetry and data quality, and mentors data scientists while setting methodological standards.

Develop, train, evaluate, deploy, and monitor machine learning and statistical models using Databricks, Python, SQL, Spark, Delta Lake, and MLflow. Prepare datasets, engineer features, manage model lifecycles, validate data, and support governance practices. Translate client requirements into scalable analytics solutions, analyze trends, and communicate findings to technical and non-technical stakeholders while collaborating across data science, engineering, cloud, security, and business teams.

Lead growth data science initiatives by partnering with Marketing, Sales, and Customer Experience leaders. Develop analyses, experimentation frameworks, and machine learning models for acquisition, engagement, retention, monetization, targeting, segmentation, uplift modeling, and lifetime value prediction. Productionize reliable models with engineering teams, communicate insights to senior stakeholders, establish experimentation best practices, and mentor junior data scientists. The role requires strong business judgment, advanced technical expertise, and experience operating in ambiguous, zero-to-one environments.

Drive product and business impact through statistical modeling, experiment design, Bayesian marketing analysis, machine learning, NLP, LLM applications, and classifier implementation. Build growth metrics, monitoring pipelines, evaluation datasets, and AI trigger systems across search and Duck.ai. Analyze privacy-constrained data, deploy production-grade models, optimize SQL queries, and lead complex projects from scoping through production with minimal direction.

Lead the development of statistical and machine learning models for time-series and geospatial data. Build analytic solutions, datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and reports. Evaluate algorithm performance, optimize classifiers, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders to deliver reliable mission-focused software and data capabilities.