Top Remote Machine Learning Jobs in Indianapolis, IN

Leads Lexia’s business analytics and data strategy function, managing analysts and overseeing data engineering alignment. Owns BI strategy, Tableau governance, data governance, self-service dashboards, AI-enabled analytics, metric definitions, and data quality. Partners with Sales, Marketing, Customer Success, executives, and analytics engineering to translate business needs into insights, KPIs, models, and actionable recommendations. Manages analytics tools and vendors while ensuring responsible, secure, and effective use of enterprise data.

Leads technical enablement and sales success for strategic national channel partners. Aligns partner and company go-to-market strategies, develops business plans, conducts technical and sales training, supports evaluations and configurations, and serves as a trusted technical advisor throughout the sales cycle. Builds executive relationships, coordinates training and marketing activities, gathers partner requirements, mentors technical managers, and collaborates with sales, systems engineering, and channel teams.

The Director of Data owns the company’s data platform, governed metrics, client identity resolution, data quality, privacy controls, and AI-ready datasets. This leader manages the data team, establishes governance and operating standards, oversees warehouse and pipeline architecture, and ensures reliable data for Finance, leadership, products, and ML/LLM systems. The role requires hands-on technical leadership, roadmap ownership, stakeholder communication, and accountability for data reliability, lineage, security, and platform costs.

Leads strategy, roadmaps, backlogs, discovery, delivery, and continuous improvement for AI-powered products. Partners with engineering, AI/ML, data, design, and business teams to translate customer needs into measurable solutions using generative AI, LLMs, agents, and automation. Manages stakeholders, product metrics, responsible AI considerations, and complex consulting engagements, including PMO and multi-team delivery responsibilities.

Leads the architecture, design, and implementation of enterprise AI solutions. Defines AI strategies, scalable architectures, technology roadmaps, governance practices, and modern technology stacks. Partners with business and technical stakeholders, guides development teams, evaluates emerging technologies, supports consulting pursuits, and provides technical leadership throughout project lifecycles. The role also mentors architects and engineers and requires client relationship development and travel.

Lead the evolution of trading infrastructure by managing trade operations, P&L attribution, risk monitoring, data integrity, and performance reporting. Develop Python- and machine-learning-based AI agents, MLOps workflows, anomaly detection, predictive risk models, automated reconciliations, and real-time dashboards to transform manual trading operations and improve risk analysis.

Leads clinical intelligence initiatives by sizing AI opportunities, conducting data discovery, evaluating AI/ML solution performance, and measuring impact, accuracy, drift, and patient outcomes. Partners with product, clinical, engineering, compliance, and operations teams to develop measurement plans, value hypotheses, benchmarks, and scalable intelligence capabilities. The role is remote with occasional travel and requires advanced SQL, Python, clinical analytics, and population health research expertise.

Develop and deploy AI-driven applications, workflows, integrations, and ServiceNow solutions for enterprise customers. Gather requirements, configure and script ServiceNow modules, implement Now Assist and other AI capabilities, integrate external systems through REST, SOAP, MID Servers, and ETL, and support testing, documentation, deployment, incident remediation, service mapping, and event management. Collaborate with stakeholders and technical leads while maintaining consistent platform patterns.

Owns core backend computation services that produce correct, reproducible, traceable, and auditable financial results. Designs configuration-driven architectures, data-intensive services, and set-based warehouse computations. Requires strong Python, SQL, data modeling, system design, reliability, isolation, and testing expertise. Provides technical leadership through design reviews, engineering standards, and mentorship while partnering with data, AI, and product teams.

Build and own scalable data architecture, canonical data models, configurable source integrations, and reliable pipelines for AI-driven financial intelligence. Use SQL, Python, dbt, cloud data warehouses, and orchestration tools to deliver idempotent, auditable data workflows. Establish lineage, data contracts, quality controls, and schema evolution practices. Lead architecture reviews, mentor distributed data engineers, and support customer-hosted multi-cloud deployments with strict tenant isolation and traceability.

Designs and builds highly scalable, fault-tolerant Azure compute services, microservices, REST APIs, billing pipelines, and distributed systems. Responsibilities include improving availability, security, scalability, performance, disaster recovery, and compute utilization; using telemetry and data-driven insights; developing cloud-native solutions with containers, NoSQL, and Big Data technologies; and leveraging AI-assisted development in an Agile environment.

Write technically accurate case studies, articles, product explainers, launch materials, and social content for AI engineers, researchers, and technical leaders. Interview customers, partners, and internal experts; learn HUD’s platform and workflows; explain technical products and research clearly; support founder communications; manage content from ideation through publication; and improve content using reader and go-to-market feedback.

Own a new enterprise sales territory, build pipeline from zero, and close advisory-style security software deals with large organizations. Develop relationships with senior security leaders, engineers, and data science teams; tailor sales engagements over lengthy cycles; coordinate demonstrations with pre-sales specialists; and involve company leadership in strategic opportunities. The role requires enterprise security software sales experience, AI and machine learning fluency, startup experience, and proximity to New York City or northern New Jersey.

Own a new enterprise sales territory, build pipeline from scratch, and close complex security software deals through long consultative sales cycles. The role involves prospecting large organizations, engaging senior security executives and technical users, tailoring solutions, coordinating technical demos with sales engineers, and re-engaging prospects over time. Candidates need at least five years of enterprise sales experience, a record of closing security software deals with large buyers, startup experience launching emerging offerings, and credible knowledge of AI and machine learning.

Product Counsel will advise product, engineering, data science, marketing, compliance, and sales teams throughout the product lifecycle. Responsibilities include counseling on regulatory compliance, privacy, intellectual property, consumer protection, AI and machine learning, data rights, platform architecture, APIs, identity and account security, and marketing. The role translates legal requirements into technical guidance, supports AI governance, monitors evolving laws, and develops scalable legal processes for a fast-paced technology company.

Leads DISCO’s machine learning research strategy, directs advanced algorithm and methodology development, oversees large-scale research programs, and manages research teams. Collaborates with industry partners and cross-functional stakeholders to apply machine learning to real-world challenges. Develops long-term research plans, mentors researchers, optimizes research processes, and contributes to the broader research community through publications, presentations, and industry engagement.

Develops predictive models, machine learning solutions, data pipelines, statistical analyses, and internal data applications. The role uses Python and SQL Server to analyze structured and unstructured data, applying NLP, classification, regression, clustering, automation, and AI/LLM techniques. Responsibilities include maintaining data science platforms, supporting cross-functional decision-making, and communicating technical findings to non-technical audiences.

Develops and deploys production machine learning models and data products for enterprise use. Responsibilities include data mining, feature engineering, model evaluation, NLP and LLM/RAG solutions, API and pipeline integration, cloud-based deployment, model validation, A/B testing, documentation, and lifecycle management. The role translates business needs into scalable data science solutions and collaborates with product, engineering, and business stakeholders throughout the SDLC.

Build end-to-end features for marketing and growth SaaS products, including interactive user interfaces, CRUD applications, backend APIs, analytics platforms, observability tools, and ML integrations. Collaborate with customers, designers, product managers, and ML engineers to translate real-world needs into intuitive products and experiments. Independently architect frontend systems while contributing to backend data models and API design.

Leads financial strategy, planning, forecasting, and operating cadence across Sales, Marketing, Customer Success, and GTM Operations. Owns GTM economics, sales compensation, commissions forecasting, executive reporting, driver-based modeling, resource allocation, and profitability analysis. Modernizes Finance through AI, automation, advanced analytics, improved controls, and scalable processes while developing the GTM Finance team and advising senior executives.

Leads automated scoring and natural language application research for educational assessment products. Responsibilities include setting team priorities, mentoring scientists, designing and analyzing studies, developing machine learning applications, collaborating with stakeholders, presenting technical findings, writing reports, publishing applied research, and supporting products involving automated scoring, crisis-content detection, and formative feedback.

Lead and grow a data science team building production machine learning models. Oversee the full model lifecycle, including data sourcing, feature engineering, training, deployment, and monitoring. Coach technical staff, manage planning and executive communication, investigate fraud patterns, improve model performance, and deliver dependable production code for real-time decisions. Present analyses that guide data strategy, product direction, operations, marketing, and sales.

Leads customer-facing technical delivery from demonstrations and requirements discovery through architecture, implementation, testing, and production launch. Builds AI-driven agents, analytical products, predictive modeling workflows, and data pipelines; integrates solutions with client CRMs, consumer applications, and varied data sources. Partners with client technical teams, presents to senior stakeholders, and feeds field learnings back to the core platform team. Requires occasional travel and strong Python, TypeScript, architecture, cloud, and communication skills.

As a Research Engineer, you'll develop and manage data systems for text-to-speech projects and enhance machine learning models using innovative strategies.

Design, build, and support high-throughput, low-latency LLM inference and data pipelines; collaborate with data scientists to post-train and evaluate models; implement scalable, production-ready systems with strong testing, monitoring, and performance focus; mentor engineers and iterate on architecture, reliability, and user-facing AI applications.