Top Remote Data Science Jobs in Hartford, CT

Own the reliability and quality of an AI copilot for trading platforms. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement loops. Partner with engineering and product on safe tool interactions, auditability, and user trust. Develop trading-domain expertise and measurement systems covering correctness, safety, latency, regression risk, and workflow performance.

Lead complex modeling projects combining deep learning, LLMs, and traditional techniques. Prototype, deploy, and monitor production models, mentor data scientists, collaborate with engineering/product, and translate results into business impact for insurance customers.

Own Junction’s data science research, statistical modeling, experimentation, and evaluation strategy across diagnostics, clinical workflows, and AI-enabled products. Build predictive and decision-support models, establish measurement and quality frameworks, partner with engineering to deploy reliable systems, and communicate findings to technical and executive stakeholders. The role combines strategic roadmap ownership with hands-on analytical work in a high-ambiguity startup environment.

Provides strategic and technical leadership for advanced credit-risk data science. Owns the multi-year modeling roadmap and independently develops, implements, validates, monitors, and improves machine learning models for credit acquisition, underwriting, portfolio management, and customer risk. Applies advanced statistical and ML techniques, conducts fair-lending and explainability analyses, researches emerging methods, and communicates modeling strategy and results to executive and cross-functional stakeholders.

Leads a team of 10+ data scientists supporting underwriting transformation through forecasting, predictive modeling, anomaly detection, financial validation, reporting, and scenario optimization. Sets data science strategy, embeds analytical capabilities into products, defines metrics and OKRs, validates business outcomes, and delivers executive insights. Partners with Product, Engineering, Underwriting, Finance, Actuarial, and executive stakeholders while developing people leaders and managing organizational priorities.

Analyze large datasets, identify meaningful patterns, develop machine learning models, and deliver actionable insights for business decisions. Collaborate with cross-functional teams to understand data needs, communicate findings clearly to stakeholders, and stay current with advances in data science and AI technologies.

Conducts multi-omics analyses of organoid and normal tissue data, including single-cell and bulk RNA sequencing, proteomics, and metabolomics. Develops computational pipelines for processing, quality control, statistical analysis, and organoid benchmarking. Collaborates with wet-lab teams on experimental design and protocol optimization, interprets findings, contributes to manuscripts, and presents research at scientific conferences.

Develop data pipelines, organize and analyze structured and unstructured government data, create visualizations, build AI agents and lightweight automations, and occasionally develop predictive models. The role involves consulting with senior stakeholders, communicating actionable insights, supporting data capacity building, and collaborating with product, design, engineering, and research teams in a remote government-services environment.

Own the reliability and quality of an AI copilot for a trading platform. Build evaluation frameworks, benchmarks, quality gates, monitoring, incident response, and model improvement workflows. Partner with engineering and product to create safe, auditable tool interactions and improve assistant behavior. Develop domain expertise in trading, risk, margin, execution, and portfolio reasoning while operating across model, backend, and frontend systems.

Analyze recruiting and interview data to improve AI-powered candidate evaluation systems. Build metrics, statistical and machine learning models, LLM evaluation datasets, experiments, dashboards, and reproducible data workflows. Collaborate with product and engineering teams on candidate matching, interview quality, recruiting analytics, bias detection, and AI system improvements.

Performs data science projects involving statistical modeling, machine learning, forecasting, data cleaning, exploratory analysis, and AI-assisted workflows. Builds and maintains models, pipelines, reporting, and analytics-ready datasets using Python, SQL, Snowflake, and dbt. Supports validation, documentation, testing, governance, and stakeholder communication while developing proficiency in modern data science tools and deployment practices.

Develop marketplace intelligence, forecasting, ranking, recommendation, personalization, and experimentation models for Airbnb’s search and marketplace products. Partner with product and engineering teams to launch AI-driven improvements, build scalable modeling pipelines, evaluate product changes, and guide technical direction. The role requires causal inference, machine learning, strong SQL and Python or R skills, cross-functional communication, and autonomous leadership of data science roadmaps.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Build and deploy statistical and machine-learning models for forecasting, risk prediction, retention, no-show prediction, experimentation, causal inference, and clinical outcomes. Partner with analytics engineering and technology teams to productionize models using monitored data pipelines. Translate ambiguous clinical and operational questions into rigorous analyses, communicate recommendations to cross-functional stakeholders, and establish data science and responsible AI practices.

Supports the design, validation, and operationalization of predictive, machine learning, and AI models. Analyzes structured and unstructured data, develops algorithms, validates outcomes, creates visualizations, and communicates findings to stakeholders. Collaborates cross-functionally to integrate solutions and prepares model pipelines for business use. The role also involves statistical analysis, NLP, exploratory data analysis, feature engineering, and research into emerging technologies.

Partner with product, engineering, and business teams to analyze player behavior, product performance, growth opportunities, and investment decisions. Apply experimentation, forecasting, behavioral analytics, and statistical methods to improve acquisition, engagement, retention, monetization, and lifecycle outcomes. Build reusable analytical tools, influence product roadmaps, define performance metrics, and collaborate with data engineering on telemetry, data quality, and measurement infrastructure.

Develop and maintain enterprise databases, data models, metadata frameworks, governance processes, ETL workflows, and data quality systems. Perform forecasting, scenario analysis, and risk assessment using complex datasets. Build automation tools, support data integration, recommend improvements, and collaborate with technical and nontechnical stakeholders. The role supports U.S. Navy data systems and requires active Secret clearance.

Build and own marketing measurement frameworks using media mix modeling, incrementality testing, and multi-touch attribution. Design experiments, conduct power analyses, validate models, reconcile measurement sources, and translate findings into client budget recommendations. Clean and integrate advertising, analytics, and commerce data, develop reusable code and templates, present statistical results to technical and nontechnical audiences, and support multiple clients across varied measurement environments.

Leads data science strategy for a federal healthcare data platform. Designs data collection, validation, and analysis tools; develops predictive models; manipulates large datasets; and collaborates with scientists, trainees, and IT teams on cloud, interoperability, integration, security, and compliance solutions. Communicates technical recommendations to varied stakeholders and supports federal data security, ATO, and information management requirements.

Develop and deploy statistical and machine learning models, conduct exploratory analysis, forecasting, segmentation, experimentation, and AI initiatives. Translate business needs into analytical frameworks, KPIs, predictive insights, and recommendations. Collaborate with data engineering, BI, QA, and business teams to ensure data readiness, model integration, quality, governance, and auditability. Support revenue, marketing, financial, and operational decision-making through scalable analytical solutions.

The Data Scientist will transform real-world, clinical, and preclinical healthcare data into reproducible datasets, analyses, and evidence supporting medical-device research and regulatory needs. Responsibilities include cohort development, data-quality validation, statistical programming, study design support, analysis plans, clinical and post-market analyses, documentation, and communicating findings to technical and nontechnical stakeholders. The role collaborates with Scientific, Clinical, R&D, Regulatory, Quality, IT, and external data partners.

Conduct actuarial and predictive analysis supporting product development, rating, reserving, pricing, financial analysis, and process improvements. Develop predictive models, multivariate analyses, tools, workflows, and scalable data processes. Collaborate with actuaries, product managers, vendors, and business units to translate complex data into strategic business solutions while ensuring accuracy and regulatory compliance.

Evaluates AI-generated data science and quantitative reasoning outputs; develops prompts, datasets, reference solutions, and objective evaluation benchmarks. Identifies statistical errors, flawed methodologies, and unsupported conclusions while providing structured feedback to improve model accuracy. Responsibilities include dataset validation, statistical and quantitative assessment, quality assurance, technical documentation, and clear communication of analytical findings.

Designs challenging data science and machine learning tasks, builds reproducible environments and verified reference solutions, and develops automated tests and evaluation frameworks for AI-generated code. The role analyzes AI agent performance, identifies bugs, leakage, overfitting, and methodological weaknesses, documents technical requirements, and iteratively improves task reliability and complexity.

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.