Top Data Science Jobs

Develop predictive, descriptive, and prescriptive models using member, financial, demographic, geographic, and behavioral data. Analyze market opportunities, membership growth, product adoption, and engagement to support branch strategy. Apply statistics, machine learning, forecasting, and trend analysis; build automated analytical processes; communicate insights to stakeholders; and collaborate with business and geospatial partners. Lead small projects and support ethical AI and data-driven decision-making.

Leads advanced analytics, artificial intelligence, predictive modeling, simulations, forecasting, and optimization initiatives supporting Navy readiness modernization. Develops solutions for supply chain, sustainment, inventory, and operational readiness; establishes model governance and validation practices; translates operational needs into recommendations; mentors data professionals; collaborates with government stakeholders; and presents findings to senior leaders.

Leads data science initiatives for US Card acquisitions, developing and deploying machine learning models across their full lifecycle. Partners with data scientists, software engineers, product managers, and business stakeholders to solve complex problems, analyze large-scale numeric and textual datasets, validate models, and translate technical findings into business outcomes. The role also involves team leadership, talent development, model governance, monitoring, and ongoing model improvements.

Leads data science initiatives for Capital One’s Card Mainstreet business. Partners with data scientists, software engineers, and product managers to develop customer-focused solutions; analyzes large-scale numeric and textual data; builds, validates, and implements machine learning models; translates technical findings into business outcomes; and supports innovation and team talent development.

Lead advanced analytics, statistical modeling, machine learning, and generative AI initiatives for Microsoft’s sales, marketing, and support platforms. Translate business needs into measurable problems, develop robust models and reusable code, evaluate and optimize GenAI solutions, diagnose model issues, design experiments, and deliver statistically reliable insights. Collaborate with engineering, product, and data science teams to drive product improvements and AI transformation.

Develop and deploy enterprise machine learning and data science solutions across the full lifecycle. Responsibilities include predictive modeling, statistical analysis, feature engineering, data exploration, pipeline development, model monitoring, MLOps, cloud deployment, and stakeholder collaboration. The role supports regression, classification, clustering, forecasting, recommendation, and NLP applications using large-scale structured and unstructured data.

Design and deliver production-ready AI, machine learning, NLP, generative AI, and RAG solutions for scientific knowledge discovery and decision support. Build LLM workflows, retrieval and ranking systems, integrate scientific metadata and ontologies, and establish evaluation and monitoring frameworks. Write Python code, collaborate with engineering teams on scalable deployment, and provide technical leadership and mentoring.

Early-career Data Scientist supporting supply chain transformation through machine learning, forecasting, statistical modeling, data preparation, dashboards, and AI solutions. The role addresses inventory optimization, demand forecasting, production planning, delivery performance, and cost reduction. Responsibilities include developing analytical datasets and workflows with Python and SQL, evaluating models, integrating enterprise data, supporting cloud-based analytics, and communicating insights to business stakeholders in a manufacturing environment.

Develop and evaluate machine-learning, statistical, and generative AI models for personalized college, career, and pathway recommendations. Analyze experiments and student data, define product metrics, build scalable pipelines, and move models into production. The role emphasizes responsible AI, fairness, transparency, privacy, explainability, and collaboration with product, engineering, research, design, and business teams.

Build statistical learning models, research external data, prepare datasets, collaborate with underwriting and actuarial teams, communicate model results, and assist with data pipelines for insurance product modeling projects.

Analyzes structured and unstructured data using statistical methods, Python, machine learning, and artificial intelligence. Develops algorithms and systems, cleans and parses data, and translates complex datasets into actionable insights for clients across public and private sectors. Collaborates with clients and team members in fast-paced environments, including work involving intelligence processes and classified data.

Develop predictive and statistical models using Python, perform data analysis with Python, SQL, and Excel, and provide actionable insights to business stakeholders. Responsibilities include data preparation, model validation, deployment support, performance monitoring, ad hoc reporting, and collaboration with Data Engineering. The role supports pricing, occupancy, operational performance, and portfolio decision-making while ensuring analytical rigor, reproducibility, documentation, and clear communication of technical findings.

Develop and deploy deep learning models for fraud detection, risk management, and identity verification. Work across diverse data modalities, including tabular data, text, point clouds, and images, throughout the machine learning lifecycle. Responsibilities include data exploration, feature engineering, model training, evaluation, deployment, monitoring, and communicating results. Collaborate cross-functionally, make technical decisions independently, and apply emerging AI and machine learning techniques to production challenges.

Develop and deploy dynamic pricing and offer-selection models using statistical, machine learning, and optimization techniques. Analyze large datasets, conduct simulated and live experiments, evaluate causal and observational results, and communicate recommendations to Product, Engineering, and executive stakeholders. Collaborate cross-functionally to formalize business problems, write production code, and influence pricing roadmaps.

Analyze disparate personnel datasets, develop Qlik or Tableau dashboards, create data manipulation scripts, identify trends, and translate findings into recommendations for senior leadership. The role also supports data management, enterprise analytics, AI products, secure handling of sensitive personnel information, data pipelines, technical writing, and business reporting. Required technologies include Databricks, Python, Spark, Pandas, SQL, and Qlik. An active Secret clearance and U.S. citizenship are required.

Develops data-driven solutions by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Builds predictive models, algorithms, data pipelines, dashboards, and interactive visualizations using Palantir Foundry and Power BI. Works with clients and cross-functional teams to translate complex data into actionable insights and guide technical development. Requires experience with data science programming, database-driven applications, and classified environments.

Conduct research-oriented data analytics and algorithm development across diverse datasets and scientific domains. Apply machine learning, data mining, statistical methods, and neural network architectures to generate stakeholder insights. Curate and exploit small and large datasets, collaborate with subject-matter experts, document technical progress, prioritize tasks, and present results. Work may span embedded systems, cybersecurity, remote sensing, modeling and simulation, and scientific computation.

Lead measurement, experimentation, and optimization for checkout, payments, subscriptions, pricing, and monetization infrastructure. Define metrics and guardrails, design experiments and causal studies, improve conversion and retention, develop pricing and localization frameworks, partner with Data Engineering on trusted datasets and monitoring, and translate analysis into product and business decisions. Provide technical leadership and mentorship across data science teams.

Develops machine learning, statistical, graph-based, and data-mining algorithms for national security analytics. Builds and validates descriptive, predictive, and prescriptive models; develops LLM and NLP solutions; creates visualizations and experiments; extracts and transforms data; collaborates with subject matter experts; and integrates prototype analytics into production analyst workflows. The role also guides analytic development efforts and requires an active TS/SCI clearance with polygraph.

Develop and operationalize Python-based data science solutions supporting DoD and Intelligence Community missions. Integrate structured and unstructured data from APIs and enterprise repositories, build analytic pipelines, dashboards, visualizations, and interfaces, and modernize legacy workflows. Analyze large datasets, create automated processes, architect scalable solutions, document workflows, and identify patterns to improve intelligence production, analytic rigor, transparency, and mission effectiveness.

Evaluates and improves statistical models, machine learning algorithms, and analytic workflows supporting classified Space and Counterspace intelligence missions. Analyzes complex datasets, validates model performance, assesses data quality, recommends enhancements, integrates new analytic approaches, and prepares technical assessments for government stakeholders. The role collaborates with engineers, developers, and data science teams in a classified environment while ensuring security and data governance compliance.

Develop data science algorithms, ETL pipelines, automated analytic software, statistical and machine learning models, and data visualizations. Collaborate with clients and teammates to interpret complex structured and unstructured data and deliver actionable insights. The role requires Python or R, version control, data engineering, visualization, and an active TS/SCI clearance with polygraph willingness. Preferred experience includes intelligence disciplines, deep learning, LLMs, distributed computing, and DevOps.

Analyzes complex structured and unstructured data for national security missions, using Palantir platforms and statistical programming to develop predictive models, workflows, visualizations, and actionable intelligence. Collaborates with clients, engineers, analysts, and mission stakeholders to understand requirements, solve complex problems, and advise on data-driven decisions. Requires an active TS/SCI clearance and willingness to undergo a polygraph.

Develops data science solutions for an intelligence community agency by integrating disparate data, performing exploratory and quantitative analysis, creating visualizations and dashboards, and applying machine learning to produce actionable insights. The role requires Python, SQL, ETL, data communication skills, and TS/SCI clearance. Preferred experience includes Apache Spark, JEMA, Flask, JavaScript, cloud services, APIs, geospatial data, and supervised or unsupervised machine learning.

Leads data science initiatives by analyzing complex structured and unstructured datasets, developing ETL pipelines, predictive models, machine learning algorithms, and automated analytic software. Collaborates with clients, guides teammates, and translates data into actionable insights. Requires Python or R, statistical and machine learning expertise, version control, TS/SCI clearance with polygraph eligibility, and a bachelor's degree. Preferred experience includes intelligence disciplines, deep learning, LLMs, DevOps, data visualization, and ontology development.