Top Data Science Jobs

As a mid-level data scientist, you'll analyze and visualize data, develop predictive models, and lead solutions to complex problems for clients in various sectors.

Design, validate, and productionize analytics and dashboards for property & casualty and employee benefits. Collect, clean, and analyze insurance data, build statistical solutions, prototype analytics, and collaborate with engineers and business stakeholders on Azure Databricks.

Design, build, validate, and operationalize analytics, dashboards, and data products for property & casualty and employee benefits. Clean and integrate insurance data, perform exploratory analysis, prototype models, and collaborate with engineers and business stakeholders to deploy solutions on Azure Databricks.

Build, validate, and operationalize analytics, dashboards, and applications on Azure Databricks for Property & Casualty and Employee Benefits. Collect, cleanse, and analyze insurance data, create visualizations, prototype solutions, and collaborate with engineers and stakeholders to drive data-driven decisions and improve data quality.

Build, validate, and operationalize analytics and dashboards on Azure Databricks for Property & Casualty and Employee Benefits. Collect, cleanse, and integrate insurance data, perform exploratory and statistical analysis, prototype analytics solutions, and communicate insights via visualizations to stakeholders while collaborating with engineers and business teams.

Build and maintain financial data models and scalable reporting systems powering ARR, revenue, billings, and other metrics. Partner with Finance, Accounting, Investor Relations, Engineering, and Data teams to ensure data accuracy across close processes, forecast and analyze performance, and operationalize key financial metrics to support executive decision-making and public company reporting.

Analyze large, diverse datasets (claims, hazard, geospatial) and apply advanced data science, ML, and AI to model catastrophe, hazard, exposure, and man-made risks. Evaluate vendor models, develop internal risk views and tools, collaborate with domain experts, and support underwriting decision-making and account-specific risk analyses.

Partner with Brand Marketing and cross-functional teams to build end-to-end measurement frameworks, attribution and causal models, and dashboards that evaluate brand and channel impact. Apply statistical and ML techniques to audience targeting, creative effectiveness, and campaign measurement, and translate insights into strategic recommendations to optimize marketing spend and improve measurement infrastructure.

The Data Scientist designs and develops statistical and machine learning models, collaborating across business functions to improve operations through data analytics and AI solutions.

The Data Scientist will support intelligence operations by applying advanced analytics to complex datasets, develop models, and collaborate with teams to enhance mission delivery.

The Data Scientist role involves analyzing complex datasets, developing models, and communicating findings to stakeholders, supporting mission needs through teamwork and data-driven insights.

As a Data Scientist, you'll lead high-stakes analytical work, creating predictive models and defining frameworks for experimentation and evaluation within a healthcare tech startup.

The role requires a Data Scientist with strong SQL and analytics experience to analyze business data and provide insights using BI tools and Cosmos.

The Data Scientist will develop solutions using SAS, R, and TSQL, with expertise in SSIS, ETL, and statistical analysis, while also having knowledge of MS tools.

The Data Scientist will apply advanced machine learning techniques, lead and mentor junior data scientists, and develop innovative data solutions on cloud platforms.

Onsite Data Scientist supporting technical targeting and data exploitation: must manipulate high-volume structured and unstructured datasets, build analytic products, and develop/validate methodologies for large-scale parallel processing across clustered computing environments using ECL/HPCC and languages like Python, R, Pig, Java, C, and SQL. Active TS/SCI with Full Scope Polygraph required.

Lead design and execution of data science projects, partner with stakeholders to define solutions, manage data acquisition and quality, develop and deploy ML models (including deep learning and NLP), monitor model performance, present findings to executives, mentor team members, and influence data governance and best practices.

Use data exploration, cleaning, modeling, and visualization to turn structured and unstructured IoT and other data into actionable insights. Develop predictive models, algorithms, and visualizations, work directly with clients, and apply ML/AI/NLP techniques to solve problems across public and private sectors.

Perform data exploration, cleaning, analysis, and visualization; develop predictive models and algorithms on structured and unstructured data; apply ML/AI/NLP techniques to deliver client-facing solutions. TS/SCI clearance required.

Embed within GTM Marketing to design experiments, build attribution, propensity, churn, segmentation, and conversion models; analyze product engagement and organic search signals; partner with analytics engineers to productionize models; and translate statistical results into actionable business recommendations.

Design and run advanced statistical and machine learning analyses on large healthcare datasets to evaluate programs, inform policy, and produce visualizations and reports. Collaborate with clinical, policy, and IT teams, ensure data quality and privacy compliance, mentor junior analysts, and translate findings into actionable recommendations to improve care delivery.

Own analytics projects end-to-end: write production Python code, design and analyze experiments to optimize CTV ad campaigns, build reporting and analytics tools, and translate insights into the Data Science product to support stakeholders and improve campaign performance.

Lead data science work for a CTV/adtech platform: write production Python code, design and analyze experiments, build reporting and analytics tools, translate insights into product, and collaborate with stakeholders to optimize ad campaigns.

Proactively explore RevenueCat's data to identify product opportunities, translate ambiguous problems into analyses and production-grade predictive/descriptive models, partner with Product and Engineering to ship data-powered features (LTV prediction, experimentation, benchmarking), define statistical approaches, iterate on models in production, and communicate insights to technical and non-technical audiences.

Lead Data Scientist responsible for managing projects in data engineering, model design, and infrastructure deployment while collaborating with clients to develop data-driven solutions.