Top Machine Learning Jobs in Hartford, CT

Leads and develops a fraud analyst team responsible for investigating emerging threats, responding to incidents, and improving fraud-risk strategies. Owns investigative standards, playbooks, workflows, and tools while serving as an escalation resource for complex cases. Partners with Data Science, Customer Success, Product, Engineering, and business leaders to convert fraud insights into scalable signals, model improvements, and better customer outcomes. Supports customer conversations as a fraud subject-matter expert and coaches analysts on communicating risk decisions.

Oversee global analytics and algorithm design initiatives for a nonprofit: identify high-impact projects, coordinate with student directors, collect feedback, design and run training for developers and technical advisors, and form partnerships to support implementation. Volunteer commitment ~5-6 hours/week.

Design, scale, and optimize ClickUp's search infrastructure using OpenSearch/Elasticsearch. Improve relevance, speed, and real-time indexing, build vector search capabilities, create evaluation frameworks, and collaborate with AI/backend/product teams to integrate and troubleshoot search at scale.

Prototype and develop generative AI applications for business software, including automated model evaluation, synthetic video, and consistent image and brand asset generation. Own projects from experimentation through product delivery, combining system architecture, quantitative reasoning, product judgment, and creative LLM application development.

Lead analytics for client engagements across data onboarding, study planning, predictive modeling, production deployment, and ongoing optimization. Develop models for audience targeting, segmentation, propensity scoring, and campaign optimization, primarily for commercial pharmaceutical applications. Partner with business, analytics, and IT stakeholders, lead technical workshops, present findings to senior audiences, and manage client relationships. Build machine learning pipelines using Python, SQL, and cloud technologies, while contributing client insights to internal product development.

Develop and maintain Power BI reports, dashboards, visualizations, semantic models, and analytics solutions. Translate business requirements into scalable technical solutions, optimize queries and data models, ensure reporting accuracy and performance, and support data governance. Serve as the primary liaison for business users, provide training and troubleshooting, manage tickets, and collaborate with Data and QA teams. The role also advances AI and machine learning capabilities, mentors colleagues, and contributes to automation, documentation, and code reviews.

Owns strategy for a defined market segment, shaping go-to-market plans, coordinating cross-functional teams, driving product adoption and revenue, and building executive customer relationships. The role uses customer and industry insights to guide product priorities, sales and services strategies, retention, and market positioning for AI-driven clinical trial software.

Design, deploy, and scale production AI applications, agentic workflows, LLM integrations, and intelligent automation across enterprise business functions. Build cloud-native services, retrieval and vector database solutions, evaluation and monitoring frameworks, AI guardrails, and reusable engineering components. Integrate AI with enterprise systems, optimize model performance and infrastructure, and support secure, governed, reliable production systems while collaborating with technical and business stakeholders.

Conduct applied AI and machine learning research focused on multimodal data, foundation models, agents, evaluation, and data enrichment. Monitor research trends, prototype emerging techniques, design experiments, validate opportunities, and translate findings into briefs and recommendations. Partner with product and engineering teams to guide data strategy, training corpus generation, and longer-term opportunity prioritization.

Leads the AI Engineering team across applied AI features and foundational ML infrastructure. Responsibilities include managing Senior and Staff engineers, hiring and coaching, deploying LLM-powered capabilities, building MLOps and AI observability platforms, balancing product delivery with platform investment, and ensuring privacy, compliance, evaluation rigor, and clinical safety in production systems.

Set the technical vision for data science, lead complex statistical and machine learning model development from concept through production, and architect scalable modeling platforms and pipelines. Develop credit risk, pricing, and loss forecasting models; establish model governance standards; partner with senior leaders; mentor data scientists; and communicate technical strategy to executives. The role emphasizes production deployment, monitoring, cloud architecture, and high-impact decision-making across the business.

Own the clinical infrastructure and program portfolio powering Arine’s medication management platform. Evolve the clinical recommendation engine, expand data inputs, standardize program configuration, and create repeatable frameworks for launching and scaling clinical programs. Partner with clinical, engineering, data science, implementation, and customer success teams to translate needs into requirements, improve onboarding, maintain regulatory alignment, and prioritize work using clinical safety, user value, and business impact.

You will execute and improve insurance workflows, analyze operational efficiency, collaborate with product teams, and support onboarding and documentation efforts.

Lead client engagements involving AI and machine learning solutions. Translate customer outcomes into technical direction, define requirements and success measures, review architectures and data pipelines, coordinate engineering delivery, and manage project planning, budgets, forecasts, and workstreams. The role requires technical fluency across APIs, cloud platforms, CI/CD, and ML deployment, plus experience delivering custom software and data solutions for complex, multi-stakeholder customers.

Supports the development, testing, and evaluation of AI/ML models for educational products. Responsibilities include data collection and cleaning, feature engineering, model validation, analyzing outputs, identifying optimization opportunities, documenting findings, preparing technical reports, and collaborating with Data Science, Engineering, and Product teams. The intern also explores applications in personalized learning, content recommendations, adaptive learning, NLP, generative AI, and responsible AI.

Leads enterprise real-world data strategy at Bristol Myers Squibb, translating R&D and business priorities into data investment, acquisition, evaluation, and evidence-generation strategies. Serves as the team’s AI innovation lead, identifying applications for generative AI, machine learning, and advanced analytics. Evaluates datasets, vendors, partnerships, and emerging technologies; develops executive-ready business cases; and drives cross-functional alignment across strategy, data, governance, customer success, and R&D teams.

Leads enterprise technology strategy, architecture, modernization, security, reliability, AI adoption, and execution. The VP will oversee engineering and data organizations, develop technical leaders, establish roadmaps and performance standards, guide cloud and data platform evolution, and connect technology investments to measurable business outcomes. This executive partners with the CEO and cross-functional leaders while managing organizational performance, operational risk, governance, resilience, and technology investments.

Lead the demand generation function by translating go-to-market priorities into segmented campaigns across email, search, social, content, webinars, and events. Own HubSpot automation, Salesforce integration, lead scoring, funnel analytics, marketing-sourced pipeline, budgets, and agency performance. Collaborate with Sales and Commercial Operations to improve lead handoffs and conversion. Apply AI and automation to increase execution efficiency while coaching team members and building the future demand generation team.

Drive adoption of AI-powered utility solutions through pre-sales consulting and post-sales enablement. Gather requirements, architect and demonstrate computer vision and ML workflows, lead RFI/RFP support, guide deployments and integrations, complete security audits, deliver customer training, and create technical presentations. The role requires strong AI/ML knowledge, customer-facing communication, scripting experience, and willingness to travel domestically and internationally.

Assist with building recommendation models, data pipelines, feature engineering workflows, ranking algorithms, and machine learning prototypes for personalized music discovery. Evaluate and optimize recommendation approaches, process user interaction data, and collaborate with engineering, research, and product teams to develop real-world personalization systems.

Lead the design and development of production-grade machine learning systems, including large-scale document pipelines and agentic workflows for healthcare claims and payment integrity. Drive LLM prompt engineering, multimodal AI, RAG, orchestration, observability, reliability, and explainability. Collaborate with data science, data engineering, and product teams, reduce technical debt, improve scalability, and mentor engineers on enterprise AI system design and development.

Leads new business development, account growth, commercial discussions, client relationships, and sales operations for AI and GenAI data solutions. Partners with engineering, delivery, and customer success teams to understand frontier lab needs, structure projects, close opportunities, and expand client accounts. Requires quota-based technology sales experience, startup experience, STEM education, and strong AI/ML market knowledge.

Design and scale machine learning infrastructure for personalization, search, ranking, advertising technology, and real-time learning at consumer scale. Build distributed systems, data pipelines, feature infrastructure, model-serving systems, and low-latency decision systems. Lead architecture and zero-to-one initiatives, partner across product and engineering teams, mentor technical leaders, improve experimentation velocity, and explore LLM applications for personalization and conversational search.

Support analytics by cleaning, validating, and transforming data; build dashboards and KPI reports; perform trend, cohort, and A/B testing analyses; improve data pipelines and forecasting; present insights to cross-functional teams.

Own quality engineering for an AI-enabled decision-support system. Develop test strategy, translate acceptance criteria into automated tests, integrate coverage into CI/CD release gates, and perform functional, integration, performance, and load testing. Build evaluation sets, audit model decisions, measure errors and overrides, document release evidence, and maintain rollback criteria. Coordinate user acceptance testing and independently govern releases. The role requires strong coding, statistical literacy, audit-ready documentation, active Secret clearance, and up to 25% travel.