Top Remote Data Science Jobs in Hartford, CT

Lead product analytics to inform strategy and optimize user experience: translate business questions into analysis, build reliable analytics infrastructure, run A/B tests and ML/statistical analyses, track funnels and cohorts, support 0-to-1 product launches, and communicate actionable insights to cross-functional teams.

Lead design and delivery of scalable ML systems across the full lifecycle for recommendations, demand forecasting, targeting, and personalization. Collaborate with engineers, product, and stakeholders to set architecture, metrics, and production best practices; mentor data scientists; lead experimentation and deploy/monitor models using cloud and MLOps tooling.

As a Data Scientist at SentiLink, you'll develop fraud detection models using machine learning, engage in product development, and support analysis across teams. This role emphasizes hands-on technical work and end-to-end project ownership in a fast-paced environment.

Design and train LLMs and agentic systems for cybersecurity using RLHF/RLAIF and supervised fine-tuning. Build agent architectures, tooling, retrieval/memory, and evaluation pipelines with rigorous statistical benchmarking. Optimize prompts and inference, collaborate with engineers to productionize prototypes, and drive research on agent planning, safety, and reliability.

Build and deploy statistical and machine learning models for the Toast platform. Collaborate with product, engineering, and business teams to identify opportunities, develop data-driven solutions, evaluate models, and communicate insights to stakeholders. Work in a fast-paced environment and contribute to production ML systems and experimentation.

As a Staff Data Scientist at SentiLink, you will develop fraud detection models, conduct analyses, collaborate across teams, and drive the development of financial risk products.

The Senior Data Scientist will build models and analyses, design experiments, integrate datasets, and leverage AI for improved workflows and insights in data science.

Lead AML model development, optimization, validation, and governance for transaction and customer screening across banking, brokerage, and lending. Build ML/statistical models, maintain vendor solutions, ensure regulatory compliance, document methodologies, produce AML dashboards, and support audits, risk assessments, and stakeholder communications.

Own and evolve revenue calibration models tying CX interactions to revenue, design causal inference and experimentation frameworks, build LLM-powered classification/NLP pipelines, productionize models with Analytics Engineers, define segmentation and behavioral signals, and ensure statistical rigor for executive reporting and regulatory defensibility.

As a Data Scientist, you'll lead high-stakes analytical work, creating predictive models and defining frameworks for experimentation and evaluation within a healthcare tech startup.

Lead development of production-grade fraud and identity risk signals from high-scale device, network, browser, mobile, API, and behavioral telemetry. Define evaluation methods, build scalable feature engineering, investigate adversarial signal patterns, influence telemetry and production readiness, mentor data scientists, and drive technical direction for robust, explainable, low-latency decisioning systems.

Develop ML features, models, and analytical methods from device, network, browser, mobile, session, and behavioral telemetry to detect fraud and improve identity confidence. Handle large-scale, noisy, high-cardinality data, design validations, assess drift and leakage, collaborate with engineering/product/risk for production rollout, document models and features, and mentor junior team members.

As a Data Scientist/Machine Learning Engineer, you'll finetune models, improve data quality, add new signals, and push solutions to production.

The Staff Data Scientist will own the analytical strategy for Firefox, driving data-informed decisions through measurement frameworks, experiments, and user growth analysis, while mentoring other data scientists.

The Staff Data Scientist will lead analytical strategies for Firefox, focusing on product growth, measurement frameworks, and mentoring data scientists while guiding data-driven decisions.

Design and evaluate causal inference experiments and causal ML models to measure product impact, optimize resource allocation, and inform support operations. Build, deploy, and iterate statistical/ML models, analyze tradeoffs, collaborate cross-functionally, and communicate insights to influence decisions and scale data science capabilities.

Build, evaluate, and deploy LLM/ML models and synthetic datasets to improve Community Support. Identify opportunities via data exploration, analyze structured and unstructured data, automate LLM evaluation, personalize experiences, and collaborate cross-functionally to drive product and operational impact.

Develop and apply causal inference and experimentation methods to measure and optimize marketing impact. Pull and analyze data, create features, design experiments, build end-to-end models, and collaborate with cross-functional teams. Communicate findings, lead internal white papers, and advance measurement science to inform marketing decisions.

The Data Scientist will build machine learning models to enhance claim audits, optimize performance, and improve healthcare decision-making by analyzing complex data sets.

The Staff Data Scientist will advance machine learning models to optimize claim audits and improve business outcomes in healthcare.

The Staff Data Scientist will lead the development and deployment of ML and GenAI systems to detect billing errors and fraud in healthcare payments, collaborating with cross-functional teams.

The Data Scientist will develop tools for enhancing signature management capabilities, integrate diverse data holdings, and leverage AI/ML solutions for operational support.

Develop a tool to enhance signature management capabilities and integrate diverse data holdings, leveraging AI/ML solutions to meet operational needs.

Customer-facing data scientist advising customers on experiment design, statistical methodology, and experimentation architecture; running and analyzing A/B tests; building scalable experimentation programs; creating repeatable frameworks and technical enablement; partnering with Sales, Solutions Engineering, Product, and Engineering to drive customer success.

Build and productionize ML models and data infrastructure to analyze millions of chaos engineering experiments, detect and explain failures, recommend automated remediation, and integrate AI-driven failure analysis into Gremlin's product using techniques like causal inference, graph ML, time-series modeling, and reinforcement learning.