Top Senior Data Science Jobs in Kansas City, MO

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Lead design and execution of scalable experimentation frameworks for Netflix Ads. Develop auction-based, biddable media models, solve cold-start measurement problems globally, apply causal inference and ML, partner with product and engineering, and translate analytic results into strategic recommendations to optimize marketplace dynamics and member experience.

Lead development of time-series and econometric models (ARIMA, VAR, state-space, WLS, panel data) and Bayesian probabilistic frameworks. Use causal inference and stochastic processes to produce actionable insights, work with large datasets on Databricks, communicate results to stakeholders, and mentor junior team members.

Lead advanced analytics and ML projects: perform EDA, feature engineering, statistical modeling, and build/optimize production ML and Generative AI solutions (LLMs, embeddings, RAG). Collaborate with engineering and MLOps to deploy and monitor models, create visualizations and dashboards, translate insights for stakeholders, and mentor junior team members.

Design, build, and scale detection and diagnostic ML models for PV and storage time-series data; own model lifecycle from problem definition to production and monitoring; partner with engineering and operations to deploy models and measure field impact; prototype new approaches and guide technical roadmap.

Design, develop, and deploy supervised machine learning models for credit decisioning. Collaborate with business and technical stakeholders to create data sources, improve modeling methodology, operationalize models, and manage model evaluation and risk for production systems.

Design, build, and deploy production-grade generative AI applications: orchestrate multi-agent systems, implement advanced RAG with vector DBs, fine-tune and host LLMs, optimize GenAI workflows, and deliver scalable FastAPI-based backend services with Docker and monitoring.

Design, build, and deploy production generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector databases, integrate/benchmark LLMs, fine-tune models, and deploy scalable FastAPI-based services with monitoring, CI/CD, and Dockerized production pipelines.

Design, build, and deploy production-grade generative AI systems: multi-agent orchestration, advanced RAG with vector databases, integrate/swap LLMs, fine-tune models, optimize prompts and latency, and containerize services with monitoring and CI/CD.

Design, build, and scale production Generative AI systems: multi-agent orchestration, advanced RAG with vector databases, LLM integration and fine-tuning, scalable FastAPI backends, Dockerized deployment, monitoring, and GenAI optimization for latency, cost, and reliability.

Build, optimize, and scale production Generative AI applications. Design multi-agent systems and LangGraph workflows, implement advanced RAG with LlamaIndex and vector DBs, fine-tune and deploy LLMs, and create high-performance FastAPI async backends. Improve latency, cost, and reliability, containerize services with Docker, and manage monitoring, API security, and CI/CD for ML deployments.

Design, build, and productionize generative AI solutions: advanced Python development, LLM prompt engineering and fine-tuning, agent frameworks, RAG pipelines with vector DBs, API and cloud deployment, MLOps/LLMOps, model evaluation and Responsible AI practices, and translate business needs into scalable AI applications.

Design, build, and scale production Generative AI applications: multi-agent systems, advanced RAG pipelines, LLM integration and fine-tuning, scalable FastAPI backends, vector search, monitoring, and Dockerized deployments.

Design, build, and deploy production Generative AI applications: orchestrate multi-agent workflows, implement advanced RAG with vector DBs, integrate and fine-tune LLMs, develop scalable FastAPI/async backends, containerize with Docker, and optimize GenAI systems for latency, cost, and reliability.

Design, build, and scale production Generative AI applications including multi-agent systems, advanced RAG pipelines, and local/frontier LLM deployments. Implement high-performance backends with FastAPI and async Python, fine-tune and quantize models, integrate vector databases and retrieval systems, and deploy/monitor containerized AI services for reliability, latency, and cost optimization.

Proactively explore RevenueCat data to identify customer problems and opportunities. Translate ambiguous product questions into analyses and production-grade predictive/descriptive models. Partner with Product, Engineering, and Analytics to shape roadmaps, define experimentation and benchmarking approaches, deploy and iterate on models powering customer-facing features, and communicate insights across technical and non-technical audiences.

Founding Data Scientist for RC Capital responsible for building underwriting, fraud detection, anomaly detection, and lead-qualification models; integrating risk signals into product flows; deploying and monitoring production models; analyzing cross-platform revenue data; and defining DS strategy to scale fintech products.

Lead marketplace measurement, experimentation, and causal analysis; build pricing, recommendation, and retention models; operationalize models with engineering; maintain dashboards and monitor supply-demand balance to guide product and growth decisions.

Design, train and validate supervised ML models on multi-billion claim records to score fraud risk; engineer features in-warehouse; deploy and monitor models in production; provide human-readable rationale and claim-level evidence; collaborate with subject-matter experts and stakeholders to ensure actionable, explainable outputs.

Own complex analyses and measurement frameworks for a product or business area. Design experiments and statistical/causal models, define and monitor KPIs, quantify impact, and translate findings into product and business recommendations. Partner cross-functionally, mentor junior analysts, and use tools like Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to scale analytics and ensure reproducibility and strong governance.

Lead causal inference and measurement efforts for payments, build and evaluate ML/AI and LLM-based models, optimize fraud mitigation and loss strategies, simulate interventions, and communicate findings to stakeholders to drive product and risk decisions.

Design, build, and productionize complex Bayesian and probabilistic modeling pipelines. Lead methodology and technical direction, collaborate with Product/Data/Engineering, mentor data scientists, communicate results to stakeholders, and apply hierarchical, state-space, time-series, and graphical modeling at scale with strong emphasis on uncertainty and model validation.

Develop and deploy ML-based fraud and risk models to detect financial crime and reduce losses. Design A/B tests, create risk signals and monitoring metrics, build tools and dashboards, provide technical leadership, and perform ad-hoc analyses in collaboration with Risk, Compliance, Product, and Marketing teams.

Lead research defining fabric's AI shopping roadmap: design and run quantitative studies, build models and benchmarks for AI search (AEO/GEO), run experiments, translate findings into customer-facing insights, and publish research to influence product and go-to-market strategy.

Lead end-to-end analytics for Customer Success: perform deep-dive diagnostics, build predictive models (churn/upsell), apply causal inference and experimentation, measure impact of initiatives, self-serve data pipelines and dashboards, and deliver AI/LLM solutions and actionable insights to reduce churn and drive revenue.