Top Senior Data Science Jobs

Senior Data Scientist to analyze large private markets datasets, build models and algorithms, extract data (including from unstructured text) using SQL, Python and AI tools, create visualizations, communicate insights to stakeholders, and collaborate with Product and ML teams to productionize data-driven solutions.

Lead and build Neural Earth's geospatial data science function: design and deploy Python-based geospatial models, architect production geospatial data pipelines, validate reproducible methods, translate scientific outputs into customer-ready products, and manage and mentor at least one direct report while advising customers and stakeholders.

Support NGA mission operations by applying Python and SQL to analyze GEOINT and multi‑INT data, develop and test analytic methodologies and machine learning approaches, identify patterns across complex datasets, and deliver actionable intelligence to government stakeholders while maintaining accuracy and quality.

Design, refine, and operationalize geospatial analytic models; structure GEOINT attributes into defensible scoring frameworks; automate workflows and build dashboards; integrate models with analytic platforms (ArcGIS); work with analysts and leadership to ensure tradecraft integrity, transparency, and repeatable mission-focused outputs using Python and SQL.

The Data Scientist will collaborate with teams to shape product strategy using machine learning and econometric models on large datasets, measuring outcomes and designing data-driven solutions.

The Data Scientist will conduct data research, develop analytics models, ensure data quality, and communicate actionable insights to stakeholders. Responsibilities include statistical analysis, data mining, and collaboration with business experts to enhance decision-making.

Lead design and implementation of scalable workflows to transform large geospatial/intelligence data into mission-ready analytic products. Script in Python, produce operational visualizations, provide senior-level direction and QA, and collaborate across NGA and IC to support intelligence operations.

Partner with Marketing as the embedded data science lead to build marketing measurement, attribution, LTV/ROAS/CAC models, run experiments, analyze the full funnel, create forecasts and dashboards, and influence channel strategy and budget allocation.

The Data Scientist will analyze data using machine learning and statistical methods to deliver insights, develop algorithms, and support customers with analytical discussions.

The Staff Data Scientist will lead data science projects, build ML models, develop AI solutions, and mentor junior staff while integrating data-driven insights into business operations.

As the Staff Data Scientist, you will lead the data science function, driving strategy, mentoring peers, and shaping product direction through advanced analytics and experimentation.

The Bioinformatics Data Scientist will analyze multi-omics data, develop methods, build tools, and communicate findings to support drug development decisions.

The Staff Data Scientist leads machine learning initiatives for personalization and optimization. Responsibilities include causal measurement, LTV model development, and technical leadership across data science teams, partnering with cross-functional teams to align strategies and impact revenue outcomes.

Build and deploy credit risk models for acquisition and account management. Create datasets and features from traditional and non-traditional sources, collaborate with credit, product, and engineering partners, run experiments, and monitor models in production to enable responsible lending decisions.

Use data science to source, ingest, clean, transform, analyze, and visualize structured and unstructured data in WDP. Develop dashboards and data products (Databricks, SQL, Python, Tableau/Qlik), create visual narratives, and brief senior DoW leadership to support IT portfolio management.

Design and lead development of ML and AI solutions: explore, clean, analyze structured and unstructured data, build predictive models, apply NLP/text-mining techniques, integrate enterprise systems, visualize results, and advise clients while guiding teammates.

As a Senior Data Scientist, you'll analyze complex data to provide insights, develop algorithms, and lead teams to address clients' questions using machine learning and predictive modeling methods.

The role involves using data science skills to extract insights from data, develop algorithms, and create analytics solutions for clients across various sectors, particularly in fraud detection, cancer research, and national intelligence.

Data Scientists develop and maintain AI software and systems, focusing on machine learning algorithms, model deployment, and data analysis for predictive maintenance and failure prediction.

Lead company-level measurement and causal strategy, define MAU×ARPU metrics and semantic mart, set scientific standards for experimentation and analysis, own strategic models (MMM, elasticity, forecasting), partner with engineering to productionize analytics, and raise org-wide scientific maturity through governance, templates, and upskilling.

Lead and deliver end-to-end data science projects: design, build, deploy, and monitor ML/statistical models for credit risk, pricing, collections, and fraud. Partner with cross-functional teams to integrate models into production, set model development standards, mentor engineers, and maintain reliable ML pipelines and monitoring systems.

As a Senior Data Scientist, you will develop technologies for signals exploitation, perform data analysis, and implement algorithms while collaborating with project teams.

Lead Data Scientist responsible for developing and implementing AI and ML solutions to support marketing and business engagement. The role involves technical leadership, model development, data analysis, stakeholder collaboration, and ensuring compliance with governance standards.

Lead Data Scientist will define goals and metrics for algorithm Test & Evaluation, research T&E methodology, lead vendor coordination, present analyses and results to stakeholders, and transition algorithms into software and real-time embedded systems.

Lead Data Scientist partners with stakeholders to solve business problems using data: extract SQL data, analyze with Python/R/Excel, visualize in Tableau, determine statistical significance, and present actionable recommendations. Works with data engineers, participates in Agile sprints, and attends regular in-office collaboration days in Costa Mesa, CA.