Top Senior Data Science Jobs

Owns product measurement and decision thresholds across Ascend’s financial operations platform. Responsibilities include defining accuracy, success, and exception metrics; auditing production scores; partnering with Product and Engineering on instrumentation; deploying models for confidence scoring, reconciliation, collections, and recovery; monitoring calibration, drift, and realized outcomes; designing experiments; and using product data to inform the roadmap.

Develops predictive models, quantitative analyses, data visualizations, and machine learning solutions for Department of Defense clients. Responsibilities include data exploration, cleaning, mining, statistical programming, natural language processing, text mining, dashboard development, and advising stakeholders on actionable insights. Requires experience with Maven Smart System and a TS/SCI clearance.

Develops and maintains human capital data strategies, analytics, dashboards, and data science tools for U.S. Transportation Command. Applies predictive analytics, machine learning, and AI to workforce planning, recruitment, professional development, retention, and decision support. Builds analysis-ready data foundations, analyzes trends, maps data across secure and non-classified defense networks, improves existing tools, and collaborates with users, subject matter experts, and leadership. Requires Palantir Foundry experience, U.S. citizenship, and an active DoD Secret clearance.

Develop machine learning, statistical, data mining, graph-based, and heuristic algorithms for national security applications. Build and optimize AI/ML and large language models, including information retrieval workflows. Analyze varied datasets, create feature vectors, design experiments, evaluate models using standard metrics, produce visualizations, and translate subject matter expertise into production analytics. Guide analytic development efforts and integrate prototype algorithms into analyst workflows.

Conduct workforce analytics and applied organizational research on attrition, mobility, engagement, performance, and planning. Analyze and validate HR data using SQL, R, or Python; apply statistical testing and regression; translate business questions into actionable insights; and communicate findings through visualizations, reports, and presentations. Partner with HR stakeholders while maintaining the accuracy, documentation, confidentiality, and integrity of sensitive workforce data.

Develops machine learning, statistical, data mining, graph-based, and large language models for national security applications. Analyzes datasets, creates feature vectors, prototypes and validates algorithms, builds experiments, generates visualizations, and implements analytics in production frameworks. The role collaborates with subject matter experts, optimizes GPU-based model training, supports LLM and RAG solutions, and guides analytic development efforts. An active TS/SCI clearance with polygraph is required.

Develop and operationalize machine learning, statistical, graph-based, and AI analytics for national security missions. Responsibilities include applied research, experimentation, data visualization, feature engineering, model development and validation, LLM and NLP optimization, GPU-accelerated computing, automation, production integration, and customer collaboration. The role also guides analytic efforts and translates complex data into actionable insights.

Leads data science initiatives by analyzing complex structured and unstructured datasets, developing algorithms, processing data, creating visualizations and dashboards, and delivering data-driven insights to clients. Uses Python, SQL, ETL processes, APIs, and machine learning techniques while guiding teammates and communicating findings to technical and nontechnical audiences.

Lead marketing effectiveness, experimentation, causal inference, predictive modeling, attribution, funnel measurement, and customer analytics. Build production-quality datasets, features, and models; assess data quality and instrumentation; communicate uncertainty and recommendations to executives; and coach analysts on reproducible, responsible methods. The role influences marketing investment, customer targeting, revenue, and enterprise priorities across travel, finance, operations, and customer experience.

Build Way’s data and intelligence layer across pricing, merchandising, personalization, forecasting, recommendations, and automation. Own data foundations, metrics, models, experiments, and production-ready datasets while partnering with Product and Engineering. Identify high-impact opportunities, ship measurable solutions, and establish rigorous, responsible data practices in a fast-moving startup environment.

Leads hands-on data science projects analyzing sensor, camera, and multimodal data to understand human movement and athletic performance. Applies machine learning, computer vision, signal processing, and statistical modeling; designs validated analytical workflows; builds reproducible software, datasets, and pipelines; coordinates technical execution; communicates limitations and tradeoffs; and mentors contributors across scientific and engineering teams.

Leads data science strategy across Digital Turbine’s mobile advertising marketplace. Designs experimentation frameworks, causal inference methodologies, and production machine learning models for bidding, audience segmentation, lifetime value, attribution, forecasting, and recommendations. Partners with engineering on deployment and monitoring, establishes scalable data science standards, mentors scientists, and communicates technical insights to executives. Requires extensive experience in data science or machine learning, advanced statistical expertise, cloud-scale data processing, and cross-functional technical leadership.

Develop scalable data science solutions for large structured and unstructured datasets supporting national security missions. Automate data processing, build machine learning and graph-based capabilities, and integrate LLMs into knowledge management workflows. Translate customer needs into analytical solutions, communicate findings through reports and visualizations, deploy models with engineering teams, and mentor junior data scientists. The role is full-time onsite in Reston, Virginia and requires an active TS/SCI clearance with CI polygraph.

Develop and deploy machine learning algorithms that improve real-time device identification and fraud detection. Own data science projects from experimentation through production, including supervised, semi-supervised, and unsupervised learning, real-time inference, training automation, exploratory analysis, and model-to-service integration. Contribute to engineering best practices, collaborate within a global remote team, and participate in a shared on-call rotation.

Develop and deploy traditional machine learning, GenAI, and NLP solutions for healthcare, insurance, and life sciences applications. Responsibilities include model research, validation, documentation, deployment, maintenance, experimentation, and collaboration with product, business development, ML engineering, and IT teams. The role also contributes to data science best practices, internal tooling, peer review, coaching, and translating client analytical needs into commercially viable products.

Perform quantitative analysis, develop decision-support visualizations, and build Python software for data pipeline operations on a geospatial intelligence program. Analyze EO and SAR imagery, process complex geospatial data, and support AI/ML systems through ETL, SQL/NoSQL databases, and data manipulation. The role requires prior overhead imagery experience and an active TS/SCI clearance, with desired experience in NLP, computer vision, automated collection, and statistical modeling.

Lead the design, development, deployment, and maintenance of predictive models and decision systems. Explore data sources, run experiments, prototype solutions, apply advanced analytics, and drive strategic business improvements. Communicate findings to senior stakeholders, own projects end to end, mentor junior data scientists, and contribute to coding standards and technical direction.

Applies statistical analysis, data processing, visualization, and modeling to complex structured and unstructured datasets. Develops data solutions using Python, R, SQL, and related technologies; creates dashboards and reports; maintains data integrity and governance; and collaborates with stakeholders on business or mission objectives. The role also requires cloud-native application deployment using Kubernetes, Docker or Podman, Helm, Git workflows, Linux, configuration management, and software release processes, with preferred experience in microservices, APIs, event-driven systems, PostgreSQL, Redis, and cloud-native architectures.

Analyze wafer-fabrication, equipment, sensor, metrology, and manufacturing data using statistical analysis, AI, and machine learning. Develop anomaly detection, yield prediction, root-cause, data pipeline, dashboard, and model-monitoring solutions. Integrate diverse manufacturing data sources, support experiments and process improvement, validate and deploy models, and communicate actionable engineering insights to technical and management stakeholders.

Conduct data science research for microelectronics and hardware security problems, including data collection, preprocessing, exploratory analysis, modeling, and performance evaluation. Apply machine learning and statistical techniques to signal and image datasets, communicate findings through presentations and whitepapers, support research proposals, mentor junior researchers, and collaborate with technical teams and government customers. The role requires work at a secure customer facility in Dayton, Ohio, with occasional travel.

Builds dashboards, applications, automations, ETL integrations, predictive risk models, and AI/ML solutions for unstructured data. Performs data normalization, visualization, business intelligence analysis, and risk assessment improvement. Recommends data storage and integration solutions, creates Power BI documentation, and applies SQL, R, Python, and other data science tools to federal systems and public-sector projects.

Senior Data Scientist will design, validate, and deploy scalable ML models (recommendation systems, NLP), source and ensure data quality, assess model performance, produce visualizations, collaborate with stakeholders, and mentor junior team members to deliver data-driven solutions that optimize business outcomes.

Lead data science projects from ideation through enterprise impact, building scalable and well-tested solutions that improve healthcare outcomes. Apply probability, statistics, machine learning, SQL, and Python to develop data-driven products and insights. Partner with stakeholders, assess product performance, identify strategic improvements, mitigate risks, and mentor teammates while aligning initiatives with business objectives.

Lead development of customized AI solutions for manufacturing clients. Analyze manufacturing data, build predictive models and tools, identify product opportunities, collaborate with customers and internal teams, and present data-driven findings to technical and non-technical stakeholders.

Own Fora’s growth operating model for forecasting, scenario planning, goal-setting, and strategic investment decisions. Partner with Strategy & Operations, Finance, founders, and executives to analyze business trends, size opportunities, model pricing and initiative impacts, and guide resource allocation. Mentor data team peers while establishing best practices, tools, processes, and standards.