Top Data Science Jobs

Leads and mentors junior developers while designing, modifying, testing, and deploying data solutions in Palantir Foundry. Builds data models and reports from ERP and legacy systems, translates business requirements into technical capabilities, and supports IT modernization. Requires Python, PySpark, cloud architecture, CI/CD, database design, Army domain knowledge, and an active DoD Secret clearance.

Develop and validate machine learning and predictive models, perform statistical and data analysis, conduct ETL across repositories, and assess model quality. Communate findings through visualizations and written reports, automate analytical workflows, and collaborate directly with nontechnical customers to refine requirements and deliver data-driven solutions. The role requires expertise in ML/AI, programming, cloud environments, CI/CD, and government or safety-critical systems.

Develops data extraction, ETL processes, portfolio analytics, statistical samples, dashboards, and operational reporting for Credit Review. Analyzes large commercial and consumer loan datasets, monitors asset quality and portfolio risks, supports targeted reviews and regulatory examinations, and provides data visualization and ad hoc analysis for management. The role also contributes to risk assessment, review universe development, and product enhancements.

Designs and maintains advanced analytics and data science solutions using statistical and machine learning techniques. Develops models, dashboards, forecasts, experiments, and executive-ready insights with Python, SQL, and related tools. Partners with engineering, product, and business stakeholders to operationalize analytics, support data pipelines, improve risk processes, and guide strategic decisions. Provides technical mentorship and ensures analytical work complies with governance, regulatory, and internal control standards.

Leads the development and maintenance of reliable data products, analytics frameworks, automation, APIs, and tools supporting predictive modeling and machine learning. Designs and integrates large structured and unstructured datasets, establishes data and code quality standards, automates manual processes, and partners with business and technology teams to deliver measurable analytical solutions. Also provides reporting, technical guidance, mentorship, and project planning support.

Performs advanced data and statistical analysis on structured and unstructured data, develops predictive models and machine learning algorithms, and delivers actionable insights for business growth. Builds data sets, designs experiments, conducts reporting, and translates business needs into measurable analytical solutions. Partners with stakeholders to communicate findings and drive informed banking decisions while managing risk and supporting assigned project planning.

Leads data science and MLOps initiatives, including data extraction, visualization, feature engineering, statistical analysis, machine learning model development, validation, and production deployment. Guides teams in developing scalable analytics, partners with software and cloud engineers, translates customer requirements into quantitative solutions, and oversees transitions from prototypes to production. Requires extensive experience in machine learning, advanced analytics, statistical analysis, AI, or data-focused software engineering.

Drive revenue growth and advertiser value through data science for Nextdoor’s Ads Marketplace. Responsibilities include developing ranking and optimization models, designing experiments, analyzing auction performance, building explainability feedback loops, and delivering strategic insights. The role partners with Product, Engineering, Marketing, Sales, Finance, and Operations to improve advertising products and explore AI and large language model applications.

Analyze structured and unstructured imagery data to identify anomalies, correlations, quality issues, and mission-driven insights. Develop statistical, predictive, and data-mining solutions; evaluate models and algorithms; build metadata profiles; and support data engineering and visualization efforts. Collaborate with software engineers to deploy forecasting, optimization, and recommendation models within an Agile Scrum environment supporting NGA’s GEOINT and Chief Data Office missions.

Designs and optimizes scalable data pipelines and data solutions supporting actuarial modeling and assumption setting. Ensures data quality, governance, validation, monitoring, and audit controls across complex financial-services datasets. Translates business requirements into technical solutions, integrates data sources, applies machine learning and advanced analytics, and improves platform performance. Provides technical guidance, reviews code and analysis, coaches junior colleagues, and contributes to data engineering standards and continuous process improvement.

Analyze digital fundraising and outreach data, collaborate with Customer Experience, Product, and Engineering teams, and provide insights to campaigns and users. Build experimentation workflows, visualizations, reports, monitoring alerts, reputation scores, and question-answering tools. Query large-scale data, contribute to production Python code, troubleshoot issues, and support customers with SQL while communicating effectively with technical and nontechnical stakeholders.

Build and own machine-learning models for small-business lending, including transaction classification, forecasting, anomaly detection, and revenue estimation. Deploy production models with testing, monitoring, and documentation. Partner with Credit and Product on underwriting policy, lending decisions, pricing, repayment terms, and uncertainty quantification. Establish ML standards while transforming financial data into defensible assessments of business health.

Develop predictive models, machine learning algorithms, data features, and analytics using operations data. Responsibilities include data collection and cleaning, trend and anomaly analysis, model deployment, communicating insights and cost savings to business leaders, and guiding engineering staff on machine learning projects.

Conduct exploratory data analysis, statistical research, and machine learning to identify patterns, build predictive models, and deliver actionable recommendations. Translate customer collection priorities into technical strategies, plans, and requirements. Analyze historical data, assess mission effectiveness, maintain traceability documentation, and liaise between customers and engineering teams. The role requires UNIX/Linux troubleshooting, strong communication, customer engagement, and an active TS/SCI clearance with CI polygraph.

Lead modeling, simulation, and analysis of satellite systems, sensing performance, space targets, and Space Domain Awareness missions. Engage stakeholders, lead small teams, present data-driven findings, support strategic decision-making, and develop space protection tactics, techniques, and procedures.

Lead modeling, simulation, and analysis of space sensing performance, satellite systems, and Space Domain Awareness. Engage stakeholders, lead small teams, present data-driven findings, support strategic decisions, and develop space protection tactics, techniques, and procedures.

Conduct exploratory data analysis, statistical modeling, machine learning, forecasting, and optimization to identify patterns and deliver actionable business recommendations. Develop predictive models and algorithms, reconcile complex datasets, create visualizations, and support decision-making. The role may address demand forecasting, inventory optimization, supply planning, and aircraft readiness metrics, using tools such as Python, R, SQL, Spark, and distributed computing technologies. DoD Secret clearance eligibility is required.

Conduct exploratory data analysis and statistical research to identify patterns, test hypotheses, and generate actionable business insights. Develop predictive models, machine learning algorithms, data transformations, and end-to-end data solutions using Python, SQL, Oracle, and advanced computing systems. Optimize data models, queries, and structures; establish data standards; support AI/ML systems; and collaborate with architects, developers, and business stakeholders.

Conduct exploratory data analysis and statistical research to identify patterns, test hypotheses, and generate business insights. Develop predictive models, machine learning algorithms, and advanced analytics using tools such as Python, R, SAS, or MATLAB. Apply expertise in statistics, NLP, deep learning, optimization, visualization, cloud computing, and distributed processing to solve complex data problems and deliver actionable recommendations.

Build trusted financial and business metrics, analytical datasets, dashboards, and recurring reports. Investigate performance drivers and variances, translate ambiguous questions into analyses and recommendations, support forecasting and planning, improve data quality with Data Engineering, and communicate findings to Finance and business stakeholders.

Leads organization-wide data science strategy, architecture, and AI/ML standards across multiple teams. Drives multi-year initiatives, platform and tooling decisions, scalable production systems, and measurable improvements. Provides technical leadership in generative AI, agentic systems, LLMs, deep learning, and advanced modeling. Influences executives and cross-functional roadmaps, mentors senior contributors, supports hiring, and promotes responsible AI, data governance, and innovation.

Develops and maintains data models, machine learning solutions, analytics platforms, and automated workflows for large investigative, intelligence, financial, and business datasets. The role includes data preparation, statistical analysis, data quality monitoring, requirements gathering, solution design, stakeholder collaboration, and presenting findings to technical and non-technical audiences. Supports government mission objectives and travels to field locations approximately four times annually.

Develop and validate clinical heuristics and algorithms using real-world healthcare data. Build Python profiling pipelines, automated data-quality checks, and longitudinal patient cohorts. Analyze complex clinical databases and ETL pipelines, evaluate AI, NLP, and heuristic outputs, and collaborate with clinical, labeling, engineering, and data science teams. Maintain documentation, configuration, metrics, and validation standards for transparent, high-quality clinical analytics.

Develop and implement data science models and algorithms, analyze complex datasets, ensure data quality and integrity, gather stakeholder data requirements, optimize data processes, and perform advanced statistical analysis and reporting in support of business needs.

Develops and optimizes NLP and machine learning pipelines for medical and claims data supporting VA disability claims adjudication. Responsibilities include extracting and analyzing healthcare data, tuning transformer-based models, validating performance, documenting workflows, collaborating with VA stakeholders, and supporting model integration into government systems. The role requires Python, NLP, SQL, healthcare data knowledge, and experience delivering data science projects in regulated environments.