Top Analytics Jobs in Indianapolis, IN

Lead analytics for law firm operations by writing advanced SQL and Python/R scripts, building executive dashboards, standardizing KPIs, analyzing case lifecycle metrics, identifying bottlenecks, and delivering actionable recommendations to drive capacity planning and process improvements.

Lead enterprise data, analytics, and AI transformation engagements as a strategic advisor and architect. Design cloud data platforms, warehouses/lakehouses, governance, integration, and BI solutions. Oversee delivery, establish standards, mentor teams, support presales, and drive measurable business outcomes across technologies like Oracle, Snowflake, Databricks, and Microsoft Fabric.

Lead and build a centralized enterprise analytics organization combining clinical, claims, demographic, and operational data to standardize KPIs, create dashboards, develop predictive models, support performance management, enforce data governance, and deliver analytic products for clinical, finance, and operational stakeholders.

Lead design and governance of enterprise ontologies, knowledge graph architecture, ingestion pipelines, and semantic layers. Enable semantic interoperability across data products, implement inference and reasoning, and establish standards, versioning, and data contracts to support scalable graph-driven analytics and AI augmentation.

The Senior Staff, Advanced Analytics will drive data strategy for the guest app, collaborating with stakeholders to improve conversion and create a multi-product experience through advanced data analysis and experimentation.

The Senior Finance Analytics Engineer designs dbt models, collaborates on metrics with finance teams, and enhances data for AI consumption while maintaining SOX compliance.

Design and maintain scalable data models and pipelines for analytics. Collaborate with teams to ensure reliable data supporting business insights.

Own and improve dbt models, ELT pipelines, and warehouse architecture (Snowflake or equivalent) to deliver reliable executive KPIs and metric governance. Build data spine for Finance/Operations, enforce data quality/testing, partner cross-functionally with Engineering, FP&A, and Accounting, and influence analytics engineering standards, tooling, and roadmap.

Design, build, and maintain dbt models, ELT pipelines, Looker explores and visualizations. Drive analytics strategy, data discoverability, observability, lineage, and security. Mentor engineers, collaborate cross-functionally, apply software engineering best practices, and integrate AI to scale data products and self-service BI.

Lead a team of data engineers and analysts, define the data engineering roadmap, oversee data infrastructure, ensure analytics delivery, and establish data governance standards.

Perform quantitative analysis and model development support within an economic capital team. Clean and explore large datasets, run basic diagnostics, build visualizations, and assist in validating predictive and ML models using Python/R/SAS and spreadsheet tools. Help integrate AI-generated code and agentic tools under manager supervision.

The Senior Data Engineer will design, build, and support data environments, orchestrate pipelines, and implement data solutions for advanced analytics and machine learning, working closely with project teams.

The Analytics Engineer will design, build, and maintain scalable data models, collaborating with various teams to ensure data quality and robust reporting, mainly using dbt and SQL.

As a Director in the Data & Analytics team, you will lead client engagements, manage projects, develop strategies, create proposals, and mentor team members in data-driven initiatives.

Manage client data and analytics projects to design and implement data strategies, automation, visualizations, and predictive analytics. Translate data into actionable insights, lead cross-functional stakeholder engagement, scope and monitor budgets, and develop internal tools and templates. Apply finance/CPA knowledge where relevant and deliver client-facing presentations.

Manage client-facing data and analytics projects: define data strategy, build automation and ETL, develop visualizations and predictive analytics, drive transformation, manage scope/budget, and communicate findings to non-technical stakeholders while leveraging finance knowledge.

Inovalon seeks candidates to join their Talent Community, emphasizing data-driven solutions to enhance healthcare outcomes and economics.

Develop and maintain value-based pro forma scenario and negotiation models, automate and standardize modeling, provide hands-on analytic support for IDN negotiations, collaborate with business partners on analytic solutions, and communicate results to stakeholders.

Embed within a core business area to define problems, build metrics/reports, run analyses and experiments, surface insights, and drive recommendations to deliver measurable business outcomes across growth, product, AI, and partnerships.

Embedded on a core business team, own analytics end-to-end: define problems, build reports and experiments, analyze data, surface insights, and drive recommendations that influence product, growth, AI/automation, or partnership decisions.

Embed with a core business unit to define problems, build metrics, run analyses, design experiments, surface insights, and drive data-informed decisions end-to-end across growth, product, AI, or partnerships.

Own analytics for an embedded business unit: define problems and metrics, build reports, run analyses, design experiments, and drive cross-functional recommendations to improve growth, product, AI, or partnerships.

Embed with a core business area to define problems, build metrics and reports, run analyses and experiments, surface insights, and drive recommendations to execution across growth, product, AI, or partnerships.

Embed with a core business unit to define problems, build metrics/reports, run analyses and experiments, and drive data-informed recommendations end-to-end to improve product, growth, AI, or partnerships.

Lead payments analytics by building scalable reporting, causal inference and ML models, designing experiments, partnering with product/engineering/finance to optimize payment processing, reconciliation, and settlement, and translating results into executive recommendations to drive global payments improvements.