Machine Learning Jobs in Stamford, CT

Lead teams applying advanced data science and machine learning to large-scale claims, clinical, and member data to generate actionable insights that reduce cost of care and improve population health. Manage client relationships, mentor staff, ensure project delivery and quality, develop payer-focused analytics products, present findings to senior stakeholders, and promote continuous improvement and knowledge sharing.

Lead AI engineering teams to design, build, and deploy end-to-end AI solutions. Manage client relationships, plan and deliver projects, mentor staff, drive adoption of cloud-native microservices, CI/CD, vector databases and orchestration tools, and ensure high standards of quality, innovation, and compliance.

Provide strategic legal counsel on data privacy, cybersecurity, and telecommunications law. Advise on incident response, cross-border transfers, privacy-by-design, compliance frameworks, contracts, AI/analytics initiatives, regulatory monitoring, and engagement with law enforcement and stakeholders.

Senior-level engineer delivering analytics and ML-Ops solutions for healthcare payers. Design and implement analytics platforms on Databricks, develop CI/CD and provisioning patterns, collaborate with data scientists, mentor juniors, and build client relationships to drive actionable insights and operational improvements.

Leads credit acquisition risk strategy for CareCredit and health and wellness portfolios. Develops and monitors credit strategies, challenger tests, risk mitigation initiatives, and new products while balancing growth and credit losses. Analyzes data from multiple systems, validates models, evaluates fraud and credit risk tools, and provides strategic recommendations to senior leadership. Requires cross-functional leadership, advanced analytics, SAS programming, statistical tools, and occasional business travel.

As a Quantitative Research Analyst, you will develop models, conduct complex data analysis, and work with stakeholders to optimize investment strategies.

Develop and implement state-of-the-art machine learning models and statistical methods using massive structured and alternative datasets to discover predictive signals, design experiments, validate ideas through simulation, and collaborate to move research into production trading strategies.

Ten-week technology internship at a multi-strategy hedge fund. Interns join a technology team and contribute to data ingestion, high-performance processing, modeling, portfolio and risk management, live execution, and post-trade analysis. The program includes strategic projects, trading simulations, mentorship, professional development, and presentations to senior leaders. Candidates should have a STEM background, programming experience, machine learning and AI knowledge, strong analytical and communication skills, and interest in financial services.

A 10-week platform internship supporting teams such as Data, Finance, Operations, Risk, Technology, Treasury, HR, Marketing, and Business Development. Interns contribute to strategic projects, automate processes and workflows, build dashboards and tools, improve operational efficiency, use AI tools, participate in a trading simulation, and present work to senior leaders.

Ten-week investment internship supporting a portfolio management team at a multi-strategy hedge fund. Responsibilities include identifying and evaluating data-driven investment insights, contributing to strategic projects, participating in a trading simulation, and presenting work to senior leaders. Interns receive mentorship, exposure to investment and platform teams, professional development, and networking opportunities.

Leads specialty exposure management for non-natural catastrophe and liability portfolios across insurance lines. Oversees analysts performing pre- and post-bind modeling, portfolio production, reporting, regulatory submissions, data quality, and model validation. Partners with underwriting teams to optimize risk-adjusted capacity deployment, presents portfolio insights to senior stakeholders, manages outsourcing relationships, and advances exposure management tools, workflows, AI, automation, and analytics capabilities. Requires extensive insurance or reinsurance catastrophe modeling experience and strong leadership, risk governance, SQL, and analytical skills.

Lead Lovesac’s paid media and customer acquisition strategy across digital and offline channels. Build and scale an in-house media team, drive AI and automation adoption, optimize omnichannel funnel performance, manage large media budgets, implement rigorous measurement (incrementality, geo-testing, MMM), and partner cross-functionally to drive ecommerce and store traffic growth.

Entry-level Associate Data Analyst supporting analytics, data science, data engineering, and project management rotations. Deliver insights, build dashboards, automate reporting, partner with stakeholders, and apply ML/LLM tools to drive product and client analytics.

Design, develop, validate, operationalize, and maintain large-scale data models and analytics. Perform end-to-end analytics including data wrangling, feature engineering, modeling (including NLP and ML), MLOps, and stakeholder communication to drive business insights and improve operational efficiency.

The Quantitative Researcher will develop and optimize trading strategies, analyze data for alpha signals, and collaborate on financial models.

Responsible for managing the full recruitment lifecycle to attract top talent in quantitative finance and technology roles, collaborating with hiring managers, and ensuring a positive candidate experience.

As a Quantitative Researcher, you'll develop trading algorithms, analyze financial data, create predictive models, and collaborate within a research team.

The role involves designing and developing tools in Python for trading and risk management, maintaining data pipelines, and collaborating with teams to enhance trading strategies using advanced techniques.

Develop and optimize machine learning and statistical models to predict liquid asset movements. Parse and analyze large datasets, implement academic research for alpha generation, and collaborate with Data, Alpha, and Strategy teams to design market-neutral trading strategies and run simulations to validate hypotheses.

Work with the Global Data Science team to analyze energy market time series, build and optimize forecasting models (ARIMA/SARIMA, XGBoost, LSTM), design data ingestion pipelines, back-test datasets, and deliver insights to traders and analysts to support forecasting and risk management.

Design and implement production-grade multi-agent LLM systems and agent workflows for advisor-facing tools. Build distributed, observable services (model serving, vector DBs, RAG) with memory/state management, validation, and compliance. Partner with product and engineering to optimize for latency, cost, and reliability, and establish evaluation frameworks to ensure grounded, auditable AI-driven insights for portfolio and advisor workflows.