Top Remote Data Science Jobs

The Lead Data Scientist will enhance analytics through predictive modeling and data engineering, focusing on quality, feature generation, and continuous learning.

Lead advanced research and predictive modeling on residential housing and energy performance data. Apply causal inference, anomaly detection, regression, and model validation to improve Pearl’s SCORE and energy models. Develop new performance metrics, evaluate climate-risk data, collaborate with external research partners, and communicate findings through white papers, publications, and clear stakeholder explanations.

Develop and migrate reporting assets from SAP BusinessObjects to QlikView and Qlik Sense. Analyze and rebuild Web Intelligence, Crystal Reports, and universes; debug legacy and new reports; optimize SQL, scripts, expressions, data models, and load processes; validate output accuracy; document migration logic; and collaborate with stakeholders on requirements and report specifications.

Lead development and productionization of telematics pricing and underwriting models. Responsibilities span statistical modeling, feature engineering, dataset and pipeline development, model deployment, monitoring, data quality validation, troubleshooting, regulatory support, and collaboration with pricing, actuarial, product, engineering, and compliance teams.

Leads consumer personalization strategy and production development for recommendations, ranking, search, browse, carts, marketing, and agentic AI experiences. Builds recommender systems, learning-to-rank models, LLM agents, evaluation frameworks, and experimentation programs. Partners with Product and Engineering on deployment, monitoring, and model quality while mentoring senior data scientists, setting technical direction, shaping roadmaps, and communicating trade-offs to executives.

Leads end-to-end machine learning model development, from business problem definition and data exploration through deployment, monitoring, documentation, and retraining. Mentors data scientists, collaborates with stakeholders and product teams, troubleshoots production code and ML pipelines, and guides Agile delivery. Requires advanced expertise in statistical modeling, machine learning, cloud platforms, MLOps, production coding, large datasets, containers, version control, and data lakes.

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.

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.

Design, build, evaluate, and productionize agentic AI, LLM, RAG, and predictive ML systems for scientific information products. Develop training and inference pipelines, fine-tune models, establish trustworthy-AI evaluation and safeguards, and operate AI capabilities at scale. Set engineering standards, lead technical initiatives, mentor scientists and engineers, influence product and executive decisions, and communicate results through presentations, publications, and client engagements.

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.

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.

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.

Leads the design, development, deployment, and validation of advanced analytics and machine learning solutions. Partners with clients and stakeholders to translate business objectives into analytical strategies, analyzes complex datasets, communicates insights, mentors technical teams, establishes model governance practices, and supports project planning, solution architecture, delivery, and business development.

Leads the design, development, deployment, and monitoring of machine learning and AI solutions. Frames complex business problems, engineers features, trains and validates models, conducts fairness and bias assessments, deploys production systems, and monitors drift and performance. Communicates results to leadership, mentors junior team members, and provides technical thought leadership on scalable ML solutions.

Leads end-to-end applied data science and AI workstreams for predictive underwriting and pricing, including problem framing, feature engineering, model development, validation, deployment, monitoring, and business-impact measurement. Builds explainable, governed, and reusable production models using healthcare and insurance data. Partners with underwriting, business, product, engineering, and legal teams; mentors others; and advances responsible AI, MLOps, model governance, and shared data science practices.

Develop analytical and AI-enabled capabilities for a federal data modernization effort. Translate stakeholder questions into statistical analyses, predictive models, forecasts, and scenario analyses; build governed datasets using Databricks, BigQuery, Python, SQL, and PySpark; validate and document models; maintain analytical products; and communicate visualizations, dashboards, findings, assumptions, and limitations to technical and non-technical audiences.

Develop and deploy data science solutions for financial crime and fraud prevention. Responsibilities include understanding, cleaning, validating, and preprocessing client data; engineering features; tuning machine-learning models; optimizing runtime performance; communicating findings; collaborating with client stakeholders and internal teams; and improving data science processes, tooling, and best practices.

Own production NLP enrichment systems for scientific literature at scale. Build pipelines extracting entities, classifications, claims, and summaries; compare classical NLP, embeddings, and LLM approaches; develop evaluations and golden datasets; and ship reliable, cost-efficient Python systems. Collaborate with data engineers using Airflow and Dagster, contribute to agentic AI applications, and partner with editors, product managers, and engineers to translate modeling needs into product outcomes.

The Senior Data Scientist will analyze data to drive product strategy, optimize performance, and support product launches, collaborating with cross-functional teams.

As a Senior Data Scientist, you'll develop fraud detection models, perform detailed analyses, and work closely with teams to drive product innovation and business outcomes.

Lead technical direction for homeowners insurance pricing and risk modeling. Architect and deploy GLM and machine learning models, including profitability, retention, conversion, and customer lifetime value models. Use geospatial data to assess localized risk, partner with actuarial, product, and engineering teams on regulatory filings and production workflows, mentor data scientists, and champion experimentation, monitoring, MLOps, and emerging generative AI applications.

Works with business stakeholders to identify data-driven opportunities, analyze trends, and support business decisions. Designs data models, machine-learning algorithms, and predictive analytics models; preprocesses structured and unstructured data; identifies data sources and automates collection; analyzes large datasets for patterns; and presents insights through data visualization.

Build and own marketing measurement, attribution, experimentation, and analytics for performance, influencer, and lifecycle marketing. Responsibilities include creating dashboards, debugging attribution across advertising platforms and CRM systems, designing incrementality and A/B tests, analyzing CAC, ROAS, payback, cohorts, LTV, and channel efficiency, developing attribution taxonomies and instrumentation, and translating findings into recommendations for marketing, product, finance, and executive stakeholders.