Top Data Science Jobs

Analyze complex datasets, develop dashboards and KPI reporting, apply statistical methods, improve data quality, and deliver actionable recommendations. The role partners with leadership and cross-functional teams on forecasting, business modeling, performance analysis, experimentation, and operational optimization. Responsibilities include querying, cleaning, validating, and transforming data; identifying trends, risks, and opportunities; and communicating findings to technical and non-technical stakeholders.

The Data Scientist will support intelligence programs by developing data-driven solutions, predictive models, and machine learning tools. Responsibilities include analyzing complex datasets, evaluating data sources, improving model and data accuracy, developing Python and API-based solutions, and partnering with stakeholders on business outcomes. The role requires SQL and large-scale data system experience, works independently, may mentor junior professionals, and is fully onsite at MacDill AFB.

Lead development of open banking data science solutions (affordability/credit decisioning, identity/income verification). Apply end-to-end data science techniques, build/deploy dashboards, run customer trials, support deployment/MLOps, collaborate with stakeholders and Mastercard data science community to drive product excellence.

Lead data-science work for matching features: translate product goals into data problems, design and analyze A/B tests, perform exploratory analysis and data mining, generate strategic insights, and collaborate with AI, product, design, and engineering to develop and iterate production ML models.

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.

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.

Senior-level data scientist responsible for developing, maintaining, and enhancing AML/CTF risk models and GenAI-enabled analytics. Collaborates with business and engineering partners to translate business problems into scalable data science solutions, monitors model performance, and communicates insights to stakeholders. Provides technical leadership, mentors teammates, and ensures compliance with regulatory and governance requirements.

Develop predictive analytics and machine learning solutions for Polaris’s Supply Chain organization, focusing on supplier risk mitigation. Responsibilities include preparing large datasets, applying statistical and machine learning methods, building and validating models, deploying solutions to cloud and edge platforms, monitoring data and model quality, and communicating actionable insights to technical and business stakeholders.

Develop and optimize machine learning and statistical models for fraud detection and anti-money laundering. Analyze large datasets to identify fraudulent patterns, build scalable data pipelines and features, evaluate model performance, and collaborate with engineering, product, and operations teams. Communicate insights to technical and non-technical stakeholders while maintaining data privacy and security compliance. The role offers significant ownership in building Oscilar’s early machine learning stack within a remote-first startup.

Lead end-to-end data science projects to enhance Parafin's underwriting engine by developing predictive models, optimizing pricing, and collaborating with engineering teams.

The Data Scientist will design and implement machine learning algorithms for enterprise applications, collaborate with federal customers, and contribute to the C3 AI Suite's features.

Oversee development, validation, monitoring, and governance of consumer credit risk models used for loss forecasting, credit decisioning, regulatory reporting, and risk management. Apply statistical, econometric, and machine learning methods to PD, LGD, and EAD models; support CECL, stress testing, and macroeconomic scenario analysis. Assess model performance, data quality, and compliance while communicating findings to senior leaders, auditors, regulators, and other stakeholders.

Develop and deploy reduced-order models and hybrid digital twins combining physics-based simulations, machine learning, and real-time IoT data. Responsibilities include building surrogate models, integrating telemetry pipelines, validating models against physical test data, deploying solutions to cloud and edge environments, and collaborating with engineering and software teams. The role also leads technical initiatives, mentors engineers, and advances simulation governance and modeling strategy.

Lead data science efforts by analyzing complex structured and unstructured datasets, developing algorithms and systems, and applying statistical and machine learning methods. Responsibilities include data cleaning, feature engineering, clustering, anomaly detection, trend analysis, and advising clients. The role also guides teammates, collaborates with clients, and translates data into actionable insights for public- and private-sector challenges.

Translates stakeholder requirements into automated, data-driven solutions. Designs end-to-end workflows for data ingestion, storage, transformation, quality, governance, and delivery across structured and unstructured data. Modernizes data environments, improves accessibility and lineage, and reduces manual processes using Microsoft 365 and related platforms. Collaborates across teams in an agile environment, prioritizes development work, tracks progress, and advises stakeholders on data strategy and AI readiness.

Designs and maintains large-scale analytic solutions using ECL and HPCC Systems. Develops data models, scalable ingestion and transformation workflows, and analytical methodologies for clustered and distributed computing environments. Uses Python, R, Pig, Java, C, and SQL for parallel data processing. Collaborates with engineers, analysts, and mission stakeholders while validating data quality, analytical methods, and outputs. Requires extensive experience supporting national security analytics.

Lead Data Scientist who partners with stakeholders to solve business problems through statistical analysis, machine learning, experimentation, causal inference, dashboards, and predictive modeling. The role involves querying large datasets, deploying and monitoring production models, automating workflows, operationalizing AI solutions, communicating insights, leading projects, and mentoring junior data scientists.

Support analytics by integrating, cleaning, modeling, and analyzing agency data; build data models and deploy data mining and statistical solutions; conduct operational research and program evaluation; create algorithms and visualizations; present findings to senior leadership; guide technical teams and provide training as systems transition to operations.

Develop and deploy scalable statistical and machine learning models for Global Payment Solutions. Responsibilities include framing business problems, building ML pipelines, conducting research, applying NLP, forecasting, optimization, Generative AI, and running A/B tests. The role collaborates with stakeholders to convert internal and external data into revenue-generating products and insights.

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.

Lead Data Scientist solving cross-functional business problems through statistical analysis, machine learning, experimentation, causal inference, predictive modeling, dashboards, and automation. The role requires developing and operationalizing models, communicating insights to technical and non-technical stakeholders, leading projects, mentoring junior scientists, and partnering with data engineers. It is hybrid, requiring residence within 100 miles of Costa Mesa, California, and regular onsite work.

Analyze credit strategies and lending performance using SQL queries, complex data aggregations, and quantitative methods. Evaluate credit policy changes, monitor portfolios, identify business improvement opportunities, and present data-backed recommendations to Risk, Operations, and other stakeholders. Collaborate with data vendors and senior leaders to support AI-driven lending and strategic decision-making.

Develop and deploy machine learning models, design experiments and A/B tests, productionalize data science code with GitHub and MLOps pipelines, and collaborate with data vendors. The role offers hands-on experience with cloud infrastructure, advanced machine learning tools, and production AI systems while working alongside senior engineering and data science leaders.

Conduct statistical analysis, media measurement, experimentation, and campaign optimization using Python, SQL, and advanced analytics. Translate client objectives into measurement strategies, manage datasets, develop reproducible analyses, create dashboards and reports, and present actionable insights. Independently manage workstreams, collaborate with data scientists and engineers, support productionization of tools, and serve as a key client contact for campaign measurement, optimization, and data infrastructure.

Conduct statistical analyses on large structured and unstructured datasets; develop algorithms, visualizations, plots, and dashboards; and lead data science projects from scoping through delivery. Translate strategic questions into analytical solutions, communicate findings to technical teams and senior DoD leaders, respond to ad hoc requests, mentor junior scientists, and establish reproducible analysis practices. The role supports strategic decision-making, risk assessment, and campaign planning for Department of Defense clients.