Top Remote Senior Level Data Science Jobs in Boston, MA

Lead end-to-end data science for customer-embedded, production solutions: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, run experiments, deploy operational models at scale, coach cross-disciplinary teams, and drive strategy, ethics, and privacy discussions.

Build statistical and machine learning models using healthcare claims, financial, organizational, and public data. Develop healthcare metrics, clinical ontologies, entity-resolution models, affiliation networks, organizational maps, sourcing methodologies, and account-targeting signals. Partner with Product, Data Engineering, Engineering, and QA to move solutions from prototype to production. The role requires substantial healthcare data expertise, comfort with ambiguity, and the ability to communicate technical concepts clearly.

Build and maintain Marketing Mix Models to measure marketing effectiveness, develop attribution metrics, and create optimization solutions for budget allocation and scenario planning. The role applies statistical, Bayesian, time series, regression, and optimization methods using Python and SQL. It partners with marketing and cross-functional teams to translate analytical findings into actionable business recommendations and influence investment decisions.

Leads rigorous evaluation methodologies for autonomous vehicle performance using statistical analysis, machine learning, simulation, and on-road data. Develops metrics, detects drift and anomalies, identifies edge cases, correlates simulation with fleet data, and strengthens launch-readiness and safety decisions. Partners with engineering, safety, and operations teams, communicates findings to leadership, establishes developer self-service tools, and mentors colleagues.

Build, train, evaluate, and deploy deep learning models for credit modeling and loss prediction. Conduct representation learning experiments, integrate models into ML pipelines, and own projects from hypothesis through measurable business outcomes. Collaborate with data scientists, engineers, and product partners, communicate findings, and develop agentic tools that accelerate data science workflows.

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.

Design and architect secure, scalable healthcare data systems; create core data models using TUVA, OMOP, and FHIR; build production data pipelines and EMR integrations; establish data quality and governance; optimize performance; deploy cloud infrastructure; and ensure HIPAA and HITRUST compliance. The role requires expert SQL and Python or Scala skills, advanced data warehouse and big-data experience, and at least eight years in data science or data engineering.

Develop fraud detection and identity-risk machine learning models from problem definition through production deployment. Responsibilities include investigating fraud patterns, exploratory analysis, feature engineering, model training and evaluation, production-quality Python and PySpark development, monitoring model and feature drift, and presenting analytical recommendations. The role requires collaboration with data scientists and engineers while addressing privacy, explainability, validation, governance, and real-world fraud-modeling challenges such as class imbalance and delayed labels.

Develops data quality checks, metadata standards, and AI use-case inventories for HHS. Cleans and integrates complex datasets, applies machine learning and NLP for automated tagging and classification, evaluates AI governance risks, and produces reporting datasets, dashboards, and recommendations. The role requires translating ambiguous federal policy and reporting requirements into repeatable analytical workflows while collaborating with product, design, engineering, and government stakeholders.

Develop and deploy machine learning and LLM-based solutions for cybersecurity challenges. Responsibilities include analyzing complex datasets, building classification and NLP models, optimizing prompting, embeddings, RAG, and fine-tuning techniques, designing evaluation methodologies, and integrating models into scalable production systems. The role requires collaboration with cybersecurity researchers, software engineers, data engineers, and product managers.