Top Data Science Jobs in Indianapolis, IN

Apply machine learning to large datasets to build predictive models, perform data mining, and develop algorithms. Analyze results, create visualizations, and provide insights to inform business decisions.

Build, monitor, and maintain production ML models and LLM/agent systems. Analyze complex datasets, develop proofs of concept, collaborate with stakeholders, and align work to sustainability business needs.

Deploy and validate NEXUS in customer environments, perform end-to-end data and feature engineering, benchmark against baselines (XGBoost/LightGBM), optimize models for business impact, handle last-mile integrations (VPC/on‑prem/air-gapped), and translate field insights into product improvements while liaising with stakeholders.

Lead product data scientist shaping growth strategy, experimentation, and metrics. Build self-service analytics, size opportunities, design and analyze experiments with product and engineering, drive activation/engagement/monetization insights, and evolve the company's experimentation culture.

Contract data scientist to remotely gather, extract, and analyze technical information about AI models, benchmarks, data centers, and companies. Tasks include reading ML papers, tracking leaderboards, processing satellite images/permits, compiling infrastructure details, and producing data insights for research and policy. Part-time (10–30 hrs/week), flexible schedule with occasional Pacific Time meetings.

As a Compliance Data Scientist Co-op, you will automate data processing, enhance Power BI reports, and utilize AI for compliance improvements.

As a Senior Data Scientist, you'll optimize search relevance, retrieve content, analyze data, and collaborate with teams to enhance product strategies.

As a Staff Data Scientist, you will lead experimentation strategies, apply advanced causal inference techniques, and guide analytical insights to drive product and business decisions.

Lead enterprise-level Medicare Stars forecasting and cutpoint analytics. Design statistical and ML/time-series models, build end-to-end Snowflake/Python/SQL pipelines, integrate LLM-enabled analytics, produce executive dashboards, and partner with Product, Clinical, and Quality leaders to translate forecasts into business strategy and risk mitigation.

Senior individual-contributor leading end-to-end analytics for Medicare Stars: designing forecasting and performance models, building enterprise dashboards (Tableau/Looker), implementing GCP data solutions, ensuring data quality, and delivering executive insights to drive Stars performance and business impact across clinical, product, and data teams.

Lead clinical data management activities: coordinate eCRF planning, database development, UAT, edit checks, external data integration, risk assessment, team coordination and training, budget oversight, SOP compliance, and process improvements for clinical studies.

Lead development of rigorous evaluation frameworks and statistical methods to measure and validate autonomous vehicle system performance. Monitor metric reliability, detect drift, identify edge cases, correlate simulation and on-road results, and feed scenarios into simulation catalogs. Partner with safety, systems, and engineering teams to inform launch decisions, build self-service tooling for developers, and mentor peers. Introduce scalable ML methods where appropriate to improve evaluation rigor and coverage.

As a Lead Data Scientist, you will design and implement scalable data pipelines and analytics capabilities while leading technical decisions across data workflows, ETL processes, and statistical modeling in the GCP environment.

The Senior Data Scientist will develop machine learning models, conduct A/B tests, and work with behavioral data to enhance customer engagement and partner ROI.

Lead data science for Yahoo Mail subscriptions: analyze acquisition, conversion, pricing, retention, and churn; design and analyze A/B and multivariate experiments; build predictive and clustering models; partner with product, engineering, design, and business teams to deliver data-driven growth and monetization insights.

The role involves deploying data science solutions in cybersecurity, collaborating with customers and teams to operationalize models and enhance security insights.

The role involves developing graph algorithms, data pipelines for identity verification, and supporting data analysis for compliance products.

Lead technical roadmap and delivery of ML and GenAI capabilities for retail pricing, demand forecasting, promotion effectiveness, and markdown recommendations. Architect GenAI agents and set model/MLOps standards, mentor distributed data science teams, partner with product and engineering, and present AI strategy to executives and customers.

Lead development and operationalization of measurement products (MMM, incrementality, insights, recommendations). Build, deploy, and maintain production-grade statistical and ML systems, debug end-to-end data and model issues, evaluate methodologies, and partner with engineering, product, and customer teams to improve measurement accuracy and reliability. Provide customer-facing explanations and support for measurement outputs.

Build and maintain large commercial datasets and predictive/forecasting models (BigQuery, dbt, SQL, Python). Design causal and experimental analyses to measure promotion impact and price elasticity. Partner with commercial, finance, and executive stakeholders to inform pricing, distribution, and investment. Deploy LLMs and AI agents to classify unstructured data and build tools that democratize data access.

The Staff Data Scientist will design and optimize deep learning models for fraud detection, mentor peers, and lead data science initiatives.

Build, evaluate, and ship matching, recommendation, and ranking models for job-seeker experiences. Work with skills and occupation taxonomies and labor-market data, partner with Engineering to productionize and monitor models, and translate messy data into clear, actionable analysis for product and customers.

Build and deploy credit and fraud risk models using advanced ML/statistical methods. Partner with stakeholders, extract insights from large transaction datasets, automate feature/production pipelines, mentor junior data scientists, and ensure model quality and deployment scalability.

Proactively explore RevenueCat's data to identify product opportunities, translate ambiguous problems into analyses and production-grade predictive/descriptive models, partner with Product and Engineering to ship data-powered features (LTV prediction, experimentation, benchmarking), define statistical approaches, iterate on models in production, and communicate insights to technical and non-technical audiences.

The Senior Data Scientist will manage data collection, design study protocols, train juniors, lead report development, and support innovative analytics tools.