Top Remote Data Science Jobs

Develop and lead data science and machine learning initiatives, including exploratory analysis, predictive modeling, experimentation, recommendation systems, user classification, model monitoring, and production deployment. Partner with product, marketing, analytics, and engineering teams to convert user insights into automated services. Manage projects throughout their lifecycle, support strategic ML planning, and deliver solutions for personalization, cross-selling, and retargeting.

Consult as a fractional data scientist for hedge funds, private equity firms, and family offices. Lead data science initiatives: build infrastructure, develop and deploy models, create portfolio dashboards, monitor post-deployment performance, and partner with investment teams to turn data into actionable investment insights. Advise on vendor selection, data quality, technology stacks, KPIs, and governance.

Builds a talent network of experienced data scientists for potential future AI evaluation projects. Responsibilities may include creating grading criteria, evaluating AI-generated or human-produced data science work, writing evidence-based justifications, applying consistent judgment, and incorporating reviewer feedback. Candidates should have professional data science experience, strong Python and SQL skills, expertise in statistical modeling, machine learning, experimentation, causal inference, and excellent technical communication.

Conduct technical research at the intersection of AI and blockchain, exploring LLMs, agents, on-chain data, trading, and intelligent applications. Build reproducible prototypes and validate innovative ideas through proof-of-concepts and product demonstrations. Track industry developments, analyze research findings, document conclusions, and collaborate with algorithm and engineering teams to advance concepts toward implementation.

Develop marketing analytics and data science models to measure campaign effectiveness, ROI, attribution, customer lifetime value, and marketing impact. Apply causal inference, experimentation, and Marketing Mix Modeling; automate model pipelines and monitoring; build insight-ready datasets and self-service dashboards; and communicate actionable findings to marketing and executive stakeholders. The role also supports campaign analysis, data requirements, training, and strategic planning.

Lead product-focused data science for Cash App Lending: define measurement frameworks, run experiments, analyze customer and risk data, use AI tools to accelerate analysis, partner with product/engineering/risk, drive technical standards, and mentor junior data scientists to inform product and policy decisions.

Lead end-to-end data science for Cash App Lending: define measurement frameworks, run experiments, evaluate model and policy performance, use AI tools to accelerate analysis, partner with product, engineering, and risk, and mentor junior data scientists to drive product and risk decisions.

Lead end-to-end data science for Cash App lending products: define measurement frameworks, run experiments, evaluate model and policy performance, detect bias/drift, build decision support, and mentor junior data scientists while partnering with product, engineering, and risk.

Lead end-to-end pricing experimentation and strategy for consumer and business products. Design and analyze A/B tests, build pricing models and production-grade data pipelines, partner cross-functionally, and present actionable recommendations to senior leadership.

Leads product measurement and analytical data-product development across compensation and workforce datasets. Owns metrics, event instrumentation, experimentation, dashboards, data quality, validation, and privacy standards. Partners cross-functionally with Product, Engineering, Data Science, Legal, Privacy, Sales, and Customer Success to translate complex usage and business data into customer-facing insights and product decisions. Provides senior analytical leadership for ambiguous initiatives from discovery through production, commercialization, and launch.

Develop and deploy machine learning models for manufacturing use cases including predictive quality, machine failure prediction, and time-series pattern discovery. Work directly with customers to scope problems, validate solutions in live deployments, and translate successful analyses into product features. Collaborate with engineering, product, and customer success teams while applying statistics, optimization, generative AI, and agentic AI to deliver end-to-end solutions.

Build LLM-powered research and analysis products for political strategists. Partner with users to shape workflows, define quality standards, design evaluations and LLM-as-judge systems, prototype features, and communicate technical tradeoffs to technical and nontechnical stakeholders. The role requires strong Python, SQL, statistical and machine learning skills, hands-on LLM and embedding experience, product judgment, and comfort with ambiguous problems.

Lead pricing modeling and experimentation to optimize margin, conversion, and retention. Build elasticity and willingness-to-pay models, design A/B and bandit experiments, develop pricing automation (DealBot) and monitoring for AI-driven rate recommendations, own ETL/analysis/visualization, and partner with Finance, Risk, Product, and GTM teams to operationalize pricing intelligence.

Develop product analytics and experimentation for Spotify’s Discovery Mode promotion tools. Build metrics, instrumentation, dashboards, and actionable insights to improve customer engagement and business outcomes. Collaborate with product, engineering, research, design, marketing, and other data science teams to identify opportunities, test hypotheses, and influence product decisions through clear data storytelling.

The Senior Data Scientist will analyze product, customer, and risk data to improve Support and Risk Operations. Responsibilities include defining metrics, forecasting demand, modeling staffing and queue health, evaluating experiments and policies, monitoring risk models for bias and drift, and communicating decisions to product, engineering, and risk stakeholders. The role also leads technical standards, develops reusable analytical tooling, mentors data scientists, and uses AI tools and agent workflows to improve analytical quality and efficiency.

Lead data science initiatives for Risk and Support Operations by analyzing customer and product data, defining metrics, forecasting demand, evaluating experiments and risk policies, and building decision frameworks. Use AI tools and agent workflows to improve analytical efficiency. Partner with product, engineering, and risk teams, communicate insights to senior stakeholders, establish technical standards, mentor data scientists, and support hiring.

Design and deploy optimization and machine learning models for allocating scarce parts and resources across competing programs. Responsibilities include formulating MILP and constraint optimization models, forecasting demand and supply risk, building data pipelines, validating outputs, communicating tradeoffs to stakeholders, mentoring junior data scientists, and partnering with software engineers to productionize scalable services.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate insights; and drive recommendations and decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, strong SQL or Python skills, persuasive communication, and ownership of ambiguous problems with measurable business impact.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independently structuring data, solving ambiguous problems, communicating recommendations clearly, and delivering measurable business impact.

Own the analytical function for a business area such as growth, product, AI and automation, or strategic partnerships. Define business questions, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independent analytical problem-solving, SQL or Python proficiency, strong communication, and ownership of ambiguous initiatives through measurable business impact.

Own end-to-end data science for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, analyses, experiments, reports, and recommendations. Translate complex findings into clear decisions, influence leadership, and drive measurable outcomes. Work independently with SQL or Python in a hands-on, remote-first environment.

Own the analytical function for a business unit spanning growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions with cross-functional stakeholders. The role requires independent data work, strong analytical reasoning, persuasive communication, and ownership of ambiguous problems through measurable business impact.

Own end-to-end data science for growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; generate actionable insights; and influence stakeholders and leadership decisions. The role requires independent data work, strong analytical thinking, persuasive communication, and ownership of ambiguous problems through measurable outcomes.

Own the analytical function for a core business area such as growth, product, AI and automation, or strategic partnerships. Define business problems, metrics, reports, analyses, and experiments; surface insights; and drive data-informed decisions and outcomes. The role requires independently structuring and interpreting data, communicating findings to leadership, and solving ambiguous problems using SQL or Python.

Own end-to-end data science for growth, product, AI, automation, or partnerships. Define business problems, metrics, reports, analyses, and experiments; communicate insights and recommendations; and drive decisions and measurable outcomes with cross-functional stakeholders. The role requires independent analytical problem-solving, strong SQL or Python skills, persuasive communication, and ownership of ambiguous initiatives.