Top Remote Data Science Jobs

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.

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.

Design, deploy, and manage machine learning models supporting client acquisition, retention, and reactivation. Develop Next Best Action, CRM, paid media, and personalization solutions using LLMs and traditional ML. Design and interpret experiments, analyze large-scale datasets, communicate recommendations to stakeholders, and collaborate with product, design, and engineering teams on product and infrastructure roadmaps.

Own end-to-end data science for emerging content surfaces, including product experimentation, causal inference, exploratory analysis, metrics, dashboards, and strategic insights. Identify opportunities to grow content supply, creators, and engagement while improving discovery. Lead content data architecture and quality initiatives, develop source-of-truth assets, and apply advanced statistical methods such as quasi-experiments and network testing. Partner with Product, Engineering, Design, and Research to influence product direction and communicate findings to diverse audiences.

Develop and maintain large housing datasets and infrastructure for US housing policy research. Responsibilities include data acquisition, cleaning, analysis, modeling, automation, visualization, dashboard development, web scraping, API use, quality control, and publication. Senior responsibilities add predictive modeling, machine learning, workflow supervision, project management, and establishing data-management best practices.

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 end-to-end data science implementations for manufacturing clients using Azure and Microsoft data technologies. Build and deploy predictive models, analyze structured and unstructured data, develop scalable architectures, and deliver actionable insights. Mentor junior team members, manage complex client projects, gather requirements, support proposals, and communicate technical findings to business stakeholders. The role also contributes to data strategy, automation, process improvement, and innovation while requiring approximately 10% travel.

Leads architecture and delivery of production-ready data science, AI, machine learning, forecasting, optimization, and analytics solutions on Azure. Partners with clients, translates business needs into scalable technical designs, guides modeling and deployment, and manages a Senior Data Scientist. Responsibilities include technical leadership, client engagement, model monitoring, stakeholder communication, solution estimation, mentoring, and delivery oversight across complex and ambiguous data environments.

Develop statistical models, experiments, causal inference methods, and scalable analytical workflows to generate business insights. Partner cross-functionally with Client Experience, Strategy, Marketing, Styling, Support, and Engineering teams to define metrics and improve products. Own analytical initiatives from framing through delivery, communicate executive-ready recommendations, mentor data scientists, and leverage LLMs and AI agents to accelerate analysis and workflows.

Lead data science engagements with customers to acquire, clean, analyze, and model data; develop scalable operational models and evaluation methods; coach engineers on best practices; drive solution strategy, ethics, and industry thought leadership; write extensible code and collaborate across teams to deliver cloud-based AI solutions.

Lead end-to-end data science projects: acquire and prepare messy datasets, develop and evaluate models tied to business outcomes, operationalize models at scale, run experiments, drive ethics/privacy, partner with customers and teams, and coach cross-disciplinary colleagues.

Build, validate, deploy, and monitor machine learning models for real-time fraud detection and financial crime risk. Ensure data quality, model reproducibility, and production reliability while collaborating with Risk Strategy, Product, and Engineering. Lead prototyping, establish best practices, and align teams across differing roadmaps. The role also supports model governance, modern data pipelines, and potential applications of LLMs and generative AI in risk and fraud detection.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Leads data science initiatives by exploring and mining data, developing advanced AI, machine learning, and statistical models, and translating complex findings into strategic business actions. Presents insights to stakeholders and senior leaders, guides solutions from ideation through delivery, coaches less-experienced professionals, and collaborates with business clients to understand needs. The role is hybrid in Columbus, Ohio, with occasional emergency-response support potentially outside normal hours.

Develops and deploys production machine learning models and data products for enterprise use. Responsibilities include data mining, feature engineering, model evaluation, NLP and LLM/RAG solutions, API and pipeline integration, cloud-based deployment, model validation, A/B testing, documentation, and lifecycle management. The role translates business needs into scalable data science solutions and collaborates with product, engineering, and business stakeholders throughout the SDLC.

Leads advanced data science initiatives by designing statistical and machine learning models, predictive analytics, forecasting, and probabilistic graph models. Develops and automates data pipelines using Python, PySpark, and R; deploys models with leading ML frameworks and MLOps tools; establishes evaluation and monitoring processes; collaborates with business teams; and mentors junior data scientists.

Build and deploy AI/ML solutions from prototype through production for CMS, including generative AI, LLMs, RAG, intelligent classification, knowledge management, agentic frameworks, and enterprise integrations. Evaluate AI coding tools, establish AI/ML roadmaps, scale adoption, mentor data scientists, and translate technical concepts for stakeholders and leadership.

Tune AML and fraud detection models using customers’ live transaction data. Investigate missed cases, alert quality, pattern drift, and false positives; validate changes through backtesting and sample review; document regulatory rationale; and present model performance and business value to compliance, risk, and data science stakeholders. Partner with customer teams on account strategy and relay customer-specific or systemic findings to product and model functions.

Lead end-to-end generative AI and machine learning initiatives, designing secure and scalable architectures, developing and deploying advanced models, analyzing healthcare data, conducting experiments, and establishing MLOps processes. Research emerging AI methods, promote responsible AI practices, and mentor or recruit AI and machine learning talent. Collaborate with software engineers to productionize models and deliver reusable capabilities for healthcare solutions.

Own mobile marketing analytics frameworks, dashboards, cohort and campaign analysis, profitability modeling, audience strategy, and incrementality experiments. Analyze attribution, retention, LTV, CAC, ROAS, payback, creative performance, and marketplace dynamics. Partner with marketing, finance, and leadership to guide spend allocation and growth decisions while navigating mobile privacy and attribution challenges.

Develops and validates machine learning and statistical models for anti-money laundering, transaction monitoring, customer screening, and risk rating. The role evaluates AML data quality, integrates vendor solutions, maintains governance and regulatory documentation, leads model lifecycle quality control, and reports performance through management dashboards. It also supports AML risk assessments, audits, regulatory examinations, and cross-functional stakeholder coordination.

Own sales forecasting and revenue analytics by developing statistical and machine learning models across products, regions, and time horizons. Lead the full machine learning lifecycle from feature engineering through deployment, monitoring, and explainability. Translate forecasts into business recommendations, deliver production data products, mentor junior data scientists, and provide technical leadership across analytics and sales teams.

Own sales forecasting and revenue analytics using statistical and machine learning models. Lead the full machine learning lifecycle from feature engineering through deployment, monitoring, and explainability. Translate forecasts into recommendations for business leaders, contribute to self-service analytics, and ship data products with analytics engineers and analysts. Provide technical leadership through mentoring, design reviews, and roadmap development.

As a Staff Data Scientist, you will analyze large-scale broadband data, develop and validate ML models, and integrate solutions into Calix Cloud, collaborating with teams for enhanced network intelligence and subscriber experience.

Designs, develops, deploys, and optimizes enterprise machine learning and data science solutions. Responsibilities include predictive modeling, statistical analysis, feature engineering, data exploration, ML pipeline development, model deployment and monitoring, MLOps, and translating business needs into actionable insights. The role collaborates with data engineers, software developers, product managers, and business stakeholders to deliver scalable solutions using cloud platforms and large-scale structured and unstructured datasets.