Top Senior Machine Learning Jobs in Providence, RI

Lead the strategic roadmap and business integration of CVS Health’s Clinical Insights Engine across screening, diagnosis, quality measurement, or care management use cases. Partner with platform product managers, clinical leaders, business owners, and technology teams to align clinical needs with scalable platform capabilities. Drive adoption, value realization, KPI monitoring, business cases, and continuous product improvement while ensuring measurable clinical and financial outcomes.

Own clinical quality for member-facing AI products by partnering with product, engineering, and clinical teams. Design clinical AI evaluations, define quality thresholds and rubrics, assess accuracy, omissions, hallucinations, medication-safety risks, and translate clinical reasoning into prompts and decision logic. Monitor evolving evidence and standards of care, validate product launches, and communicate findings to technical and clinical audiences.

Manage post-sales relationships with a portfolio of customers, primarily financial institutions, to drive adoption and business outcomes from BioCatch’s behavioral biometric solution. Responsibilities include strategic consulting, project coordination, risk and escalation management, customer presentations, quarterly business reviews, roadmap feedback, stakeholder communication, team coaching, statements of work, and identifying expansion opportunities with account managers.

Lead design and implementation of Headway's insurance systems (eligibility, pricing, matching, claims). Build data-intensive, ML-powered platforms and automation, mentor engineers, write high-impact code, and improve accuracy and reliability across state and payer complexity to scale insurance-covered therapy.

Leads business analysis, advanced analytics, KPI development, data modeling, reporting automation, and AI/ML initiatives supporting global data-center construction and operational excellence. Builds datasets, dashboards, performance metrics, forecasts, and scenario models; translates complex findings for executives; aligns technology solutions with business needs; and promotes data governance, documentation, process improvement, and analytics maturity across cross-functional teams.

Build and lead Polymarket’s fraud operations function, including investigation workflows, SOPs, case routing, escalation paths, SLAs, QA processes, and reporting. Manage Fraud Analysts and BPO partners, oversee performance and capacity, investigate fraud typologies, identify process bottlenecks, and partner with Product, Engineering, Data Science, Compliance, and Fraud Strategy to improve detection, automation, and prevention.

Design, deploy, and manage machine learning models supporting client acquisition, retention, and reactivation. Develop Next Best Action, CRM, paid media, and personalization solutions using LLMs and traditional ML. Design and interpret experiments, analyze large-scale datasets, communicate recommendations to stakeholders, and collaborate with product, design, and engineering teams on product and infrastructure roadmaps.

Own end-to-end client relationships for a book of B2B SaaS customers: drive adoption, engagement, retention, and expansion of AI-powered contract drafting tools. Lead onboarding, training, large-scale rollouts, and translate customer feedback into product improvements while designing scalable CS processes and tracking health metrics to demonstrate value.

Own global GTM enablement for a Databricks product area, creating scalable technical content, competitive narratives, demos, labs, AI-powered tools, and learning programs for Solutions Architects and partner technical sellers. Partner with Product and Engineering to translate roadmaps into field-ready assets, establish feedback loops, measure field readiness and deal outcomes, and support selected customer-facing engagements. Build enablement programs from scratch while remaining hands-on with data and AI platforms.

Design, productionize, and scale machine learning and optimization models, pipelines, libraries, and deployment platforms. Partner with data scientists, engineers, product managers, and business stakeholders to deliver ML capabilities, personalization systems, and reusable end-to-end workflows. Establish ML engineering best practices, troubleshoot data pipelines, mentor junior professionals, and help define BetterHelp’s ML platform strategy.

Manage enterprise metadata, business glossaries, data catalogs, and end-to-end data lineage for a federal case management modernization program. Configure governance and metadata platforms, integrate data sources and pipelines, establish standards and workflows, monitor metadata quality, support compliant data sharing, develop governance metrics, document procedures, and train data stewards and business users.

Own customer outcomes from discovery through production deployment. Build decision systems using ontologies, reasoning, graph analytics, optimization, and machine learning over enterprise data. Write Python, SQL, and PyRel; debug unfamiliar systems; tune performance; harden deployments; document reusable solutions; and upstream platform improvements. Lead technical workshops and proofs of concept while partnering closely with customers, Product, and Engineering.

Drive new-logo sales and revenue growth for Wonderlic’s talent assessment and employee development solutions. Identify and prospect target clients, build relationships with HR and recruiting decision-makers, manage opportunities through the full sales cycle, close new business, and transition customers to Customer Success. Maintain expertise in products, market trends, and competitive conditions while providing strategic insights. The role requires B2B SaaS and HR technology sales experience, quota achievement, pipeline generation, and experience selling to SMB, mid-market, and enterprise buyers.

Own strategic partnerships with CPG customers, driving measurable outcomes, platform adoption, user expansion, and retention. Analyze usage data and business context to identify risks and opportunities, develop success plans, lead executive and operational relationships, and advocate for customers internally. Collaborate with product, sales, marketing, and support teams while contributing to repeatable playbooks, thought leadership, and customer best practices.

Conduct applied research on scalable optimization and resource-allocation algorithms for large communication networks. Apply machine learning, reinforcement learning, integer optimization, graph-based methods, and metaheuristics to improve solution quality and efficiency. Collaborate with software engineers to integrate algorithms into production aerospace networking systems, evaluate performance at realistic scale, publish research, support patent applications, and document novel methods.

Manage the operational integrity of an automated group health insurance underwriting workflow. Review and approve RFPs, validate broker and external data, correct errors, monitor funnel and risk metrics, analyze quote-to-bind performance, triage inquiries, coordinate with sales and health plan operations, and maintain underwriting dashboards and reporting.

Lead AI agent optimization efforts by designing systems that improve token efficiency, response time, reliability, and output quality. Build agentic coding systems, develop AI application patterns, own features end to end, collaborate with engineering and field teams, support the developer community, and create public technical documentation. Work across Python, TypeScript, Java, and Go while serving as a domain expert in agent design and optimization.

Design and productionize machine learning, deep learning, LLM, personalization, recommendation, search, and retrieval systems. Collaborate with product, engineering, and analytics teams to deploy AI solutions that improve customer experiences, business growth, and operational efficiency. The role requires driving projects from ideation through deployment and delivering measurable business impact in a fast-moving fintech environment.

Leads AI/ML engineering teams across platform and delivery pods, managing employees and contractors while enforcing code, agent, review, and production-readiness standards. Partners on reusable templates, pipelines, and evaluation processes, ensures adoption across teams, manages contractor performance and conversion, and removes delivery blockers. The role requires hands-on credibility reviewing agent architectures, prompts, and evaluation suites, with experience operating as a player-coach in fast-moving organizations.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Own LanceDB’s product direction by shaping the roadmap with technical leadership, researching users and competitors, gathering continuous user insight, and driving cross-functional prioritization. The role partners closely with engineering as a technically respected peer, defines product changes for AI and data-platform users, uses metrics and experiments to guide decisions, and operates with high ownership in an ambiguous environment.

Advise enterprise clients on AI and data strategies, transformation roadmaps, operating models, governance, privacy, technology rationalization, and business cases. Lead discovery workshops, executive engagements, and art-of-the-possible sessions; prioritize use cases and connect technical recommendations to business outcomes. Build the advisory practice, develop offerings, manage multidisciplinary teams, mentor staff, strengthen client and partner relationships, and support successful distributed project delivery.

Develop new enterprise and upper mid-market business, manage client accounts, and serve as a trusted consulting advisor. Prospect and qualify leads, collaborate with technical teams to create proposals and close SOW contracts, identify business improvement opportunities, build channel partnerships, maintain CRM records, and support localized marketing activities. The role is remote within specified target markets, includes up to 10% travel, and has no direct reports.

Designs and maintains high-volume HL7 and FHIR ingestion pipelines, secure Epic and hospital integrations, data validation systems, and compliant healthcare data exchanges. Builds enterprise SaaS microservices using ReactJS, Python, and AWS services; optimizes distributed and hybrid-cloud environments; leads testing, code reviews, production troubleshooting, architecture documentation, and customer communications. Preferred experience includes Epic App Orchard, healthcare integration engines, clinical workflows, GenAI/ML, DevOps, data pipelines, and NoSQL.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.