Top Remote Data Science Jobs

Lead analytics and measurement for a Sales subdomain: monitor KPIs, investigate performance issues, design experiments, build data foundations and self-serve dashboards, use agentic AI to automate workflows, and communicate results to senior sales, finance, and operations stakeholders.

Lead analytics for a Sales subdomain: measure performance, investigate drivers, design experiments, and deliver end-to-end data science solutions. Build and maintain data foundations, self-serve dashboards, and communicate results to senior sales, finance, and operations stakeholders. Use agentic AI and modern GTM tooling to scale impact and improve decision-making.

The Data Scientist will develop predictive models, conduct exploratory analysis, collaborate with teams, build data pipelines, and create visualizations to drive decision-making.

Perform quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visuals, collaborate with cross-functional teams, use AI tools to scale analyses, and drive projects to completion to inform product decisions.

Partner with Digital Assets, Finance, Treasury, and Risk to design measurement frameworks, perform strategic analysis and modeling, build scalable SQL-driven automation and dashboards, analyze on-chain and finance data, create visualizations, and drive data-informed product decisions for USDC and related digital asset offerings.

As a Staff Data Scientist, you will lead complex marketplace problem-solving, architect ML systems, mentor teams, and drive data science strategy in partnership with engineering and product management.

Lead payments-focused data science work: build foundational datasets, metrics, and scalable analytics; analyze customer and transaction behavior (including onchain data); conduct strategic analyses to identify product and revenue opportunities; build reporting, dashboards, and automation; and influence product and business strategy through data storytelling to senior stakeholders.

Build and validate predictive models, analyze large structured and unstructured datasets, generate business insights, present analytic results to stakeholders, and develop tools to advance analytics capabilities.

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.

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.

The Data Scientist will analyze complex datasets, build predictive models, collaborate with stakeholders, and present insights for business decisions.

As a Product Data Scientist, you will analyze user behavior, drive experimentation, build data tools, and collaborate across teams to influence product strategy and innovation at Whatnot.

As a Senior Data Scientist, you will lead high-complexity projects, develop ML and NLP solutions, and collaborate across teams to drive business impact through statistical modeling and data analysis.

Lead product-focused quantitative analyses and machine learning efforts to generate actionable insights, drive product decisions, and optimize features. Collaborate with cross-functional teams to design metrics, build scalable statistical solutions, create dashboards, and communicate findings while using AI tools responsibly.

Lead experimentation and causal inference at company-wide scale: define methodology, own statistical approaches for small samples and clustered data, apply causal methods when experiments arent feasible, run experiment review, build tooling and curriculum, and partner with analytics and AI/ML teams to ensure decision-grade results.

Design, build, and deploy predictive models for risk and fraud using R, Python, SAS, and SQL. Analyze large datasets, develop cloud-based analytic solutions, validate scoring code with stakeholders, and document statistical methodologies and best practices.

Lead Oracle-focused healthcare data quality and analytics: validate transactional data, build standardized KPIs and dashboards, develop predictive inventory and revenue cycle models, and collaborate with clinical and finance teams to ensure compliance and optimize operations.

Assist senior data scientists with data collection, preprocessing, cleaning, and basic statistical analysis. Support model development, perform basic visualizations, and document processes and findings while learning and applying data science techniques.

Perform data cleansing, ETL, and preprocessing; analyze Retail CPG sales, inventory, and customer data; build predictive models for forecasting and segmentation; research and deploy Agentic frameworks and intelligent agents; produce visualizations and communicate insights to cross-functional stakeholders to optimize supply chain, marketing, and customer experience.

The Risk Data Scientist will develop adaptive decisioning models for risk assessment in financial transactions, focusing on fraud detection and compliance, while managing model governance and deployment in a regulated environment.

Senior individual contributor who drives product analytics, experimentation, and applied modeling. Owns growth and performance analyses, designs experiments with robust statistical evaluation, builds repeatable reporting, and partners with Product, Engineering, Operations, Clinical, and Finance to translate data into actionable product and operational decisions.

Support ARNG CFO analytics by preparing and facilitating PPBC-G forums, coordinating across staffs, designing and executing data analyses and ML models, and developing/enhancing websites, applications, and secure database access for program portfolio analytics and executive presentations.

Early-career data scientist role to analyze user and content data, build metrics, design recommendation strategies (recall, ranking, cold-start, diversity), run A/B tests, evaluate experiments, explore LLM/multimodal applications, and improve data pipelines and feature logging.

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

The Sr. Data Scientist will collaborate on clinical projects, translate data into actionable insights, and ensure study compliance, requiring advanced statistical computation skills.