Top Data Science Jobs in Indianapolis, IN

Design, build, and deploy LLM-based applications and multi-step AI workflows (retrieval, orchestration, agents) using large-scale structured and unstructured data. Partner with engineering to productionize solutions, implement evaluation and safety measures, ensure compliance and data governance, and communicate results to technical and non-technical stakeholders.

Lead clinical data management across studies: design and oversee eCRF and EDC implementations, coordinate testing and external data integrations, manage data risk assessments, supervise data teams and training, control related budgets, ensure SOP compliance, and drive process improvements.

Lead end-to-end delivery of strategic data science and AI solutions for AI infrastructure. Work with telemetry and experimentation at fleet scale to build, deploy, and measure predictive and prescriptive models. Partner with stakeholders, present insights, enable adoption, and promote responsible AI. Collaborate closely with data engineers and infrastructure teams to productionize scalable ML solutions.

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

Lead product and program analytics for Microsoft skilling experiences. Design funnel and learner-journey analyses, define success metrics, build measurement frameworks, run experiments and causal analyses, develop competency/skill models, partner with product and engineering on telemetry, recommend content and curriculum improvements, mentor data scientists, and ensure data privacy and telemetry quality.

Design, develop, and deploy production-grade generative AI and agentic systems (LLMs, RAG, AI agents). Build document intelligence, retrieval pipelines, vector search, and monitoring frameworks. Collaborate with risk, compliance, product, and engineering to deliver scalable, governed GenAI solutions in regulated environments.

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

The Data Scientist will build machine learning models to enhance claim audits, optimize performance, and improve healthcare decision-making by analyzing complex data sets.

Design and evaluate causal inference experiments and causal ML models to measure product impact, optimize resource allocation, and inform support operations. Build, deploy, and iterate statistical/ML models, analyze tradeoffs, collaborate cross-functionally, and communicate insights to influence decisions and scale data science capabilities.

Lead end-to-end predictive analytics and machine learning initiatives, including forecasting, churn prediction, and risk modeling. Translate business needs into scalable ML solutions, build and deploy models on AWS SageMaker, develop production ML pipelines, and oversee monitoring, governance, explainability, and retraining. Collaborate with engineering and MLOps teams, communicate insights to stakeholders, and mentor junior data scientists while providing hands-on technical leadership.

Design and analyze incrementality tests, media mix models, causal analyses, and advanced analytics for marketing measurement. Work with large marketing datasets, assess methodological validity, conduct power and sensitivity analyses, and translate findings into media planning recommendations. Collaborate cross-functionally, improve measurement standards, and mentor junior data scientists.

Lead strategy and technical direction for generative and multimodal vision systems. Build end-to-end model workflows, improve realism, controllability, and robustness, drive evaluation and production-readiness, partner with engineering to deploy optimized inference, and mentor scientists while owning a major technical pillar from research to production.

Own end-to-end data science for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; communicate recommendations; and influence product, growth, finance, and operations leaders to drive measurable outcomes.

The Staff Data Scientist will advance machine learning models to optimize claim audits and improve business outcomes in healthcare.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

Build and productionize ML models and data infrastructure to analyze millions of chaos engineering experiments, detect and explain failures, recommend automated remediation, and integrate AI-driven failure analysis into Gremlin's product using techniques like causal inference, graph ML, time-series modeling, and reinforcement learning.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; generate insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independently structuring ambiguous problems, using SQL or Python, communicating findings clearly, and delivering measurable business impact.

Develop and deploy predictive and causal models for personalization, retention, monetization, and product strategy. Design experimentation and measurement frameworks, analyze user and marketing behavior, quantify business impact, and translate complex findings into strategic decisions. Collaborate with Product, Engineering, Marketing, and Data Product teams to build scalable data solutions. Mentor peers, promote rigorous model validation and governance, and apply Python, SQL, Snowflake, dbt, Airflow, Spark, and AWS to production-ready data science initiatives.

Perform statistical analysis, predictive modeling, and data visualization on workforce and training data. Build dashboards, produce ad hoc analyses and decision-support products, advise on data governance/privacy, and collaborate with stakeholders to define metrics, deliverables, and reproducible analytic workflows.

The Forward Deployed Marketing Data Scientist will work with AI customers and engineering teams to optimize AI-driven marketing campaigns through data analysis, diagnostics, and communication of insights to stakeholders.

Use statistical and causal inference methods to build, implement, and monitor marketing models (Bayesian MMM, multi-touch attribution), analyze A/B and uplift tests, partner with marketing to test and drive data-informed decisions from large web-product datasets.

Seeking a skilled Data Scientist/Machine Learning Engineer to develop machine learning models, design AI systems, and collaborate in data analytics. Responsibilities include productionizing models, data engineering, and cross-functional collaboration.

Lead the design, development, deployment, and governance of advanced analytics, machine learning, and AI solutions using large healthcare datasets. Responsibilities include data preparation, statistical and predictive modeling, model monitoring, explainability, visualization, stakeholder communication, and translating findings into clinical and operational improvements. The role also mentors data professionals, advises leaders, manages initiative priorities and risks, and promotes responsible AI and Agile practices.

Develop and deploy machine learning models for demand forecasting and inventory optimization; conduct advanced time-series analysis; collaborate with analytics engineers and data analysts to build end-to-end data products; translate analysis into actionable recommendations for supply chain; promote data science best practices and mentor the team.