Top Remote Machine Learning Jobs in Indianapolis, IN

Develop and advance the organization’s Insider Threat Program by assessing capabilities, creating behavioral indicators, enhancing detection analytics, and investigating suspicious activity. The role analyzes security, fraud, and behavioral telemetry; configures monitoring technologies; reduces false positives; produces risk reporting and metrics; and partners with Cybersecurity, Fraud, HR, Legal, Risk, and Technology teams to improve insider-risk detection and response.

Lead research at the intersection of AI, cybersecurity, and computer systems. Define research problems, develop hypotheses, build prototypes, design rigorous experiments, evaluate AI and security methods, and translate findings into practical systems. Research areas may include AI agents, autonomous penetration testing, attack-path reasoning, cyber deception, AI safety, and security evaluation. Mentor junior researchers, collaborate with engineering and product teams, and communicate findings through reports, presentations, prototypes, or publications.

Independently lead complex technology and commercial transactions, including SaaS, cloud, licensing, vendor, partnership, and data-processing agreements. Advise cross-functional stakeholders on privacy, security, intellectual property, GDPR, AI, regulatory, and commercial matters. Improve contracting through templates, playbooks, CLM workflows, AI-enabled tools, analytics, and scalable legal operations. Analyze legislation and regulatory developments affecting cybersecurity, privacy, AI, and digital infrastructure.

Owns the commercial health and client relationships for Databricks data and AI engagements. Responsibilities include managing budgets, margins, forecasts, scope changes, executive stakeholders, steering committees, delivery transitions, business outcomes, close-outs, and follow-on opportunities. Partners with Sales, project managers, solutions architects, and technical leaders to deliver measurable client value across data engineering, lakehouse, governance, BI, and AI initiatives.

Manage the full enterprise sales cycle, from prospecting and pipeline development through deal closure and account expansion. Build relationships with executives and decision-makers, run multichannel campaigns, develop pursuit strategies, coordinate with partners and internal teams, maintain accurate forecasts, and achieve revenue targets for data engineering, platform modernization, and AI/ML consulting services.

Drive the full enterprise sales cycle for data engineering, platform modernization, and AI/ML consulting services. Build pipeline through prospecting, outreach, networking, campaigns, and events; develop pursuit strategies; close complex deals; and expand strategic partnerships. Own executive client relationships, coordinate sales, delivery, customer success, solutions engineering, and marketing, while maintaining accurate forecasts and achieving revenue targets.

Represents Snowflake to senior marketing and advertising leaders in regulated industries. Develops privacy-safe measurement, identity resolution, audience activation, personalization, AI, and data clean room solutions on Snowflake. Leads strategic customer engagements, creates reference architectures and reusable playbooks, partners with sales, product, engineering, and technology partners, and serves as a marketing and AI thought leader through workshops, enablement, webinars, and industry events.

Leads strategic engagements with CMOs and marketing executives across technology, media, and manufacturing. Translates marketing measurement, audience strategy, personalization, identity, and advertising challenges into Snowflake data and AI solutions. Builds demonstrable use cases, repeatable sales and enablement assets, and thought leadership for customers, field teams, and partners. Collaborates with sales, solution engineering, product, and ecosystem partners while advising executives and supporting strategic growth.

Develop and deploy data science solutions for pharmacy benefits and member experience, including predictive models, generative AI pipelines, and rules-based logic. Extract and analyze large-scale data with Python and SQL, manage model lifecycles, monitor production performance, and communicate insights to clinical and business leaders. Collaborate with data scientists, engineers, and operational stakeholders to solve complex healthcare and PBM problems.

Own the strategy, roadmap, backlog, and delivery of GridUnity’s AI platform capabilities. Partner with customers, engineering, design, data science, sales, and leadership to validate AI use cases, define requirements, manage experiments and releases, and measure model and business outcomes. Ensure responsible AI practices including explainability, auditability, privacy, monitoring, human review, and fallback behavior. Provide technical product expertise, support customer-facing activities, and mentor other product owners.

Supports the full AI development lifecycle by helping execute pilots and proofs of concept, documenting standards and playbooks, evaluating AI use-case pipeline health, researching tools and platforms, developing acceleration dashboards, supporting value assessments, and preparing leadership updates. The intern will also present a final project and gain experience with responsible AI, governance, and enterprise AI delivery.

Drive new enterprise business for Automation Anywhere’s Agentic Process Automation platform. Responsibilities include prospecting, managing sales pipelines, conducting customer presentations and demonstrations, attending industry events, leading proof-of-concept exercises and RFX responses, and coordinating solution design with technical teams. The role requires consultative enterprise software selling, quota attainment, technical communication, and collaboration with sales engineers, partners, and professional services.

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.

Manage strategic biopharma and digital health accounts, build executive and developer relationships, expand NVIDIA platform adoption, communicate the value of its AI and accelerated computing offerings, develop account strategies, forecast revenue, and lead complex engagements with cross-functional teams and technology partners. The role requires enterprise technology sales expertise, healthcare ecosystem experience, strong revenue performance, AI/ML knowledge, and up to 25% travel.

Own the strategy, architecture, development, deployment, monitoring, and continuous improvement of production personalization and recommendation systems. Build custom embeddings, ranking models, and shared representations across feeds, search, and recommendations. Lead ambiguous problems from concept to measurable customer impact, establish AI evaluation standards, partner with product and data teams, make cross-team technical decisions, and coach senior engineers.

Designs and implements customer solutions for a freight-loading dock sensor platform. Responsibilities include mapping logistics processes, supporting hardware and software deployments, optimizing sensor placement, identifying automation and integration opportunities, conducting ROI analyses, delivering product demonstrations, managing demo environments, and advising clients and partners on complex technical solutions. The role requires supply chain systems knowledge, strong customer-facing communication, and approximately 50% travel.

Lead statistical measurement and client intelligence initiatives using healthcare, prescription, and claims data. Develop performance benchmarks, evaluate program impact, apply statistical and machine learning methods, advise data scientists, support survey-based measurement, manage analytics vendors, mentor analysts, and present findings to marketing teams and clients. The role requires advanced SQL and Python, healthcare data expertise, vendor management, and extensive quantitative experience.

Serve as primary client contact for a portfolio of loyalty-program clients. Scope and manage end-to-end actuarial analyses rooted in predictive modeling and machine learning, validate model outputs, translate results into clear recommendations and presentations, and partner with analysts and data scientists to deliver high-quality, client-facing work in a fully remote consulting environment.

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.

Build and deploy production machine learning systems for personalized recommendations, fraud detection, risk scoring, ranking, classification, and anomaly detection. Own the full ML lifecycle, including mathematical formulation, feature engineering, training, evaluation, A/B testing, deployment, monitoring, and reliability. Develop low-latency real-time inference services using large-scale donor and transaction data, collaborate with Platform and DevOps teams, and mentor engineers on ML best practices and scientific rigor.

The General Counsel will lead the in-house legal function, advise executive leadership and the Board, manage legal teams and outside counsel, and oversee corporate, regulatory, privacy, AI, employment, intellectual property, litigation, and contract matters. The role will guide product and sales initiatives, develop scalable legal workflows, manage risk, support HR, and advise on consumer protection, fintech regulations, data privacy, and emerging AI frameworks as the company expands.

Build backend services, data pipelines, detection features, and AI agents for email security. Integrate machine learning and large language models into investigation workflows, evaluate attack detection accuracy, improve reliability and performance, and own features from design through production release. Collaborate with ML engineers, data scientists, and software engineers while contributing tested code, technical designs, and code reviews.

Lead managed services for clients' Azure, Databricks, and Microsoft Fabric data environments. Architect, implement, monitor, troubleshoot, and govern data platforms, ETL pipelines, data warehouses, semantic models, reporting, and AI/ML solutions. Manage incidents and SLAs, advise clients on licensing and technical strategy, communicate solutions, document environments, and mentor distributed engineering teams.

Leads technical discovery, enterprise solution architecture, technology assessments, and implementation roadmaps for consulting clients. Designs architectures across applications, data, cloud, integration, and enterprise technology; advises executives on technology decisions, scalability, security, cost, and risk. Guides technical teams, resolves complex delivery challenges, develops proofs of concept and reusable standards, evaluates emerging technologies, and supports proposals, estimates, statements of work, and new business development.

Build and own ClickUp’s AI evaluation systems, agent harness, self-improvement loops, model benchmarking, routing, and fallback strategies. Architect scalable backend and AI platform services for LLM deployment, orchestration, monitoring, logging, and automated evaluation. Improve reliability, privacy, security, and compliance while integrating AI features into the core product and collaborating cross-functionally.