Top Data Science Jobs

Lead and grow a 4–6 person data science team, set the roadmap, deliver marketplace forecasting and production ML (matching, scoring), embed data science cross-functionally, and establish modeling, experimentation, and monitoring best practices.

Lead client-facing data science efforts: design and develop algorithms and models, build and monitor operational data pipelines, leverage SQL/NoSQL and cloud architectures, query and migrate data via APIs, program in Python/JavaScript/Java, guide teammates, and translate complex data into actionable insights for decision-making.

The Senior Data Scientist analyzes semiconductor manufacturing data, develops predictive models, collaborates with teams for process optimization, and creates data visualizations to enhance product performance.

Develop analytic models and data processing workflows on structured and unstructured datasets; clean, transform, and enrich data; build visualizations and dashboards; integrate analytics into systems with development teams; collaborate with analysts and product owners to refine requirements; document methodologies and participate in Agile ceremonies to deliver within sprint commitments.

Entry-level Data Scientist to develop, train, and deploy statistical and machine learning models (regression, SVM, neural nets) on large datasets using R/Python/Spark. Tasks include data access and quality improvements, anomaly detection, model building, documentation, and collaborating with cross-functional teams to deliver analytic products, particularly for health insurance use cases.

Analyze large, disparate data sets to discover patterns and build data-driven solutions. Develop full-stack Python/React applications and APIs for automation, visualization, and analysis. Maintain security best practices, manage data pipelines and databases, and support analytic infrastructure to advance client missions.

Embedded on the central Data Science team, you will define the right problems, build metrics, run analyses and experiments, create reports, and drive data-informed recommendations end-to-end across growth, product, AI/automation, or partnerships.

Embedded within a core business area, own end-to-end analytics: define problems, build metrics/reports, run analyses and experiments, surface insights, and drive data-driven recommendations to influence product, growth, AI, or partnerships.

Embed with a business unit to define problems, run analyses, design experiments, build reports, surface insights, and drive data-informed decisions across growth, product, AI, or partnerships.

Embedded on a business unit, define problems and metrics, run analyses and experiments, build reports, surface insights, and drive data-informed decisions across growth, product, AI, and partnerships.

Embed with a business unit to define problems, build metrics, run analyses and experiments, generate insights, and drive data-informed decisions end-to-end to improve growth, product, AI automation, or partnerships.

Embedded in a core business area, own analytics end-to-end: define problems, build reports, run analyses, design experiments, surface insights, and drive data-informed decisions with stakeholders using SQL and Python.

Embedded in a core business area, you will define problems, build metrics and analyses, run experiments, surface insights, and drive data-informed recommendations end-to-end to improve product, growth, AI/automation, or partnerships metrics.

Embed with a core business unit to define problems, design experiments, build reports, run analyses, surface insights, and drive data-informed decisions end-to-end across growth, product, AI/automation, or partnerships.

Embedded on a business unit (growth, product, AI, or partnerships) to define problems, set metrics, build reports, run analyses and experiments, surface insights, make recommendations, and drive end-to-end execution to improve key business metrics.

Embedded on a core business team, own analytics end-to-end: define problems, build metrics and reports, run analyses and experiments, surface insights, and drive data-informed recommendations to improve growth, product, AI/automation, or partnerships.

Embed with a business unit to define high-impact problems, pull and analyze data, build reports and experiments, surface insights, and drive recommendations end-to-end to improve product, growth, AI/automation, or partnerships.

Embed on a central Data Science team to define problems, build analyses, metrics and experiments, surface insights, and drive cross-functional decisions that move product, growth, AI/automation, or partnerships metrics end-to-end.

Work with clients to transform structured and unstructured data into actionable insight using statistical methods, machine learning, NLP, data mining, predictive modeling, and visualization. Lead development of algorithms and solutions, guide teammates, and apply appropriate tools to support mission-focused projects.

Use statistical analysis, machine learning, and data engineering tools to explore, clean, model, and visualize structured and unstructured data. Collaborate with clients to define questions, develop predictive models and visualizations, and communicate insights to technical and non-technical stakeholders.

Design and apply statistical, mathematical, and computational methods to extract value from large, messy datasets. Build, evaluate, and communicate analytic and ML models, translate mission needs into technical requirements, and recommend solutions while explaining results to non-technical stakeholders.

The Data Scientist II will design, build, and deploy machine learning models and data products, translating business problems into data-driven solutions, while collaborating with various stakeholders and ensuring reliable, production-ready outcomes.

The Data Scientist will apply machine learning and statistical analysis to support mission analysis, develop analytical tools, and visualize complex datasets for insights.

The Data Scientist will develop data-driven solutions, integrate APIs, manipulate data using Python, analyze large datasets, and collaborate with teams.

The Data Scientist will design predictive models, analyze immigration data, create dashboards, and communicate technical findings to agency leadership.