Top Remote Data Science Jobs

Build and scale first-line risk control monitoring systems using statistical analysis, automated workflows, and reusable data science modules. Define risk metrics, investigate control performance, establish thresholds and guardrails, and translate analyses into monitoring applications. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, and support decision-making. Lead ambiguous, cross-functional initiatives while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Build and scale first-line risk control performance monitoring for financial products. Develop metrics, key risk indicators, statistical baselines, thresholds, automated workflows, and reusable analytical modules. Partner with risk, compliance, product, and engineering teams to improve governance, identify monitoring gaps, evaluate control effectiveness, and support decisions. Lead ambiguous initiatives from definition through adoption while communicating findings, limitations, and recommendations to technical and executive stakeholders.

Develop and productionize machine learning and AI solutions across document intelligence, multimodal systems, and foundation models. Responsibilities include dataset and pipeline development, model training and evaluation, GPU experimentation, reusable AI component design, production deployment partnership, monitoring, and communicating technical tradeoffs. The role requires solving open-ended problems in regulated financial services while emphasizing explainability, reproducibility, data governance, reliability, and measurable business impact.

Early-career Data Scientist supporting supply chain transformation through machine learning, forecasting, statistical modeling, data preparation, dashboards, and AI solutions. The role addresses inventory optimization, demand forecasting, production planning, delivery performance, and cost reduction. Responsibilities include developing analytical datasets and workflows with Python and SQL, evaluating models, integrating enterprise data, supporting cloud-based analytics, and communicating insights to business stakeholders in a manufacturing environment.

Lead a data science team to design, build, and deploy large-scale, real-time AI/ML solutions (forecasting, diagnostics, LLM chatbot, retention models, optimization) to optimize GPU cloud gaming performance and user experience.

Develop marketing effectiveness analytics using Marketing Mix Modeling, attribution, customer lifetime value, experimentation, and causal inference. Build automated, scalable model pipelines, insight-ready datasets, and self-serve dashboards. Analyze campaigns, support marketing strategy, communicate findings to executives, and lead high-impact projects independently while collaborating cross-functionally.

Own analytical strategy and develop AI/ML models for Zip’s financial products, including cash and rent advances. Lead ambiguous 0-to-1 initiatives using bank transaction data, establish evaluation frameworks, guide risk and growth decisions, and deliver models from experimentation through production. Provide technical direction to data scientists, mentor junior team members, collaborate with engineering, and advise senior stakeholders on model trade-offs. The role requires strong financial services, statistical, research, and machine learning expertise.

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 improve decision systems, predictive models, experiments, and advanced analytics solutions. Explore data sources, prototype technology-driven solutions, evaluate algorithms, and recommend strategic business changes through rigorous analysis. The role requires applying machine learning and software engineering practices to real-world product, customer, and financial problems.

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 statistical and machine learning models using healthcare claims, financial, organizational, and public data. Develop entity resolution, clinical ontology, affiliation mapping, organizational inference, account segmentation, and data-sourcing methodologies. Partner with Product, Data Engineering, Engineering, and QA to move solutions from prototypes into production. Explain technical concepts clearly and work effectively amid ambiguity.

The Data Scientist develops predictive models, segmentation frameworks, machine learning solutions, data pipelines, and experiments supporting marketing and customer analytics. The role analyzes complex datasets, validates and monitors models, communicates insights to technical and executive audiences, and partners with strategy, media, client services, and engineering teams to deliver scalable data-driven solutions.

Designs and deploys predictive models, segmentation frameworks, machine learning solutions, and data pipelines supporting marketing performance and customer analytics. Analyzes complex datasets, conducts experiments, validates and monitors models, and communicates insights to technical and non-technical stakeholders. Partners with strategy, media, client services, and engineering teams to create scalable data-driven solutions while contributing to peer review, knowledge sharing, and evolving data science practices.

Designs and deploys predictive models, segmentation frameworks, machine learning solutions, data pipelines, and experiments supporting marketing and customer analytics. Analyzes complex datasets, validates and monitors models, communicates insights to technical and executive audiences, and partners with strategy, media, client services, and engineering teams to deliver scalable data-driven solutions.

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.

Provides technical leadership for document verification machine learning solutions. Oversees the full ML lifecycle, including data strategy, model development, evaluation, deployment, monitoring, and improvement. Mentors data scientists, establishes technical standards, identifies risks, and improves model performance, scalability, and reliability. Partners with engineering, product, and customers to translate needs into modeling strategies and communicates technical decisions to diverse stakeholders.

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.

Develops machine learning models and production data science pipelines to improve subscription conversion, content recommendations, user understanding, workflow automation, and product decisions. Leads analytical projects and experiments, applies statistical methods to web-scale data, and collaborates with data, product, growth, and engineering teams. The role also communicates complex findings to diverse audiences and helps foster a strong data science culture.

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.

Measures customer engagement impact on pipeline, renewals, account health, and predictive model performance. Conducts baseline, holdout, before-and-after, and control-group analyses; validates customer data and helps tune models. Builds reusable value analyses, dashboards, and reporting for leadership, Customer Success, and Data Science. Requires strong SQL, Python, statistics, experiment design, business communication, and experience translating messy data into trusted insights.

Develop and standardize analytical methods for inferring residence, displacement, and poverty from mobile network operator data. Assess data quality, write technical specifications and tests, guide operator engineering teams, tune statistical methods, and balance accuracy with implementation constraints. Design privacy-preserving analyses, improve data security practices, train internal teams, and support international workshops and emergency-response programs.

Lead data science and AI consulting engagements for technology, media, and entertainment clients. Develop Agentic AI solutions, statistical models, experiments, KPIs, and advanced analytics to solve business problems. Manage client relationships, teams, staffing, quality assurance, stakeholder communication, and executive presentations. Support proposals, business development, thought leadership, training, and growth of the analytics practice.

The Senior Data Scientist will conduct product analysis, build advanced models and analytics tools, evaluate A/B tests, develop automated dashboards, and generate recommendations for product strategy. The role partners with business stakeholders and Engineering to improve data infrastructure and governance, communicate statistical insights, and support data-driven decisions across Product, Marketing, and Finance.

Own product-led growth analytics across activation, engagement, conversion, monetization, and retention. Lead experimentation, statistical analysis, causal inference, metric development, and proactive opportunity discovery. Build scalable analytical systems, monitoring frameworks, semantic models, and AI-powered tools. Partner with Product, Engineering, and leadership to shape product bets, investment decisions, and roadmap priorities. Work autonomously in a remote-first environment and communicate complex findings through clear, actionable recommendations.

Develop and own advanced machine learning and AI models using bank transaction and cashflow data to enable financial product capabilities, manage risk, and drive profitable growth. Design experiments, build proofs of concept, monitor production models, and collaborate with data and machine learning engineering teams on scalability. Communicate findings and tradeoffs to business stakeholders while applying statistical, research, and financial services expertise.