Top Data Science Jobs

Uses advanced analytics, business intelligence, and data science to deliver strategic insights and solutions for AML and sanctions clients in banking and capital markets. Responsibilities include analyzing complex datasets, developing data pipelines and architecture, creating scalable visualization solutions, collaborating with teams, managing stakeholders, building client relationships, and solving complex problems. The role also involves mentoring and managing others while maintaining professional and technical standards.

Lead development of interactive safety visualizations, dashboards, analytical applications, data marts, and automated reporting for autonomous-driving safety. Build trusted, performant data products with quality monitoring, frequent refreshes, metric definitions, lineage, and statistical interpretation. Partner with safety engineers, data engineers, program teams, and executives to translate complex safety information into clear decision-support tools and continuous improvement insights.

Develop scalable data science and statistical analytics solutions to monitor emerging vehicle safety hazards. Analyze large structured and unstructured datasets, detect anomalies and trends, support safety investigations and root-cause analysis, automate recurring workflows using Python, SQL/Spark, and Databricks, and communicate findings through visualizations and presentations to technical stakeholders and senior leadership.

The Data Scientist Intern will support Network program execution through data modeling, transformation, analysis, visualization, reporting, and process tooling. Responsibilities include building operational insights and business narratives, improving program observability and accountability, supporting compliance and auditability, collaborating with cross-functional stakeholders, and contributing to special projects. The intern will also participate in mentorship, training, and professional development activities.

Develop and productionize GenAI, LLM, machine learning, and analytics solutions for ASIC semiconductor engineering. Build agentic systems, RAG workflows, predictive models, and failure-analysis tools; deploy and monitor AI solutions in cloud and enterprise environments; collaborate with architecture, design, data science, and IT teams; and communicate technical findings while identifying opportunities to improve engineering productivity, quality, cost, and cycle time.

Develop GenAI, machine learning, deep learning, and analytics solutions for ASIC semiconductor engineering workflows. Build LLM and agentic systems using prompting, RAG, Graph-RAG, benchmarking, and evaluation. Deploy and monitor production AI/ML solutions in cloud and enterprise environments, collaborate with architecture, design, data science, and IT teams, and communicate technical insights to diverse stakeholders.

Leads cybersecurity data science initiatives, including exploratory analysis, statistical modeling, machine learning, deep learning, feature engineering, model governance, and secure production coding. Partners with cybersecurity, engineering, and business stakeholders to define use cases, deploy reliable models, validate AI-assisted recommendations, establish AI workflow guardrails, and address security, resiliency, auditability, and regulatory requirements.

Leads data science and machine learning initiatives addressing cybersecurity and technology risk. Responsibilities include analyzing security datasets, developing and evaluating statistical and deep learning models, engineering features, preparing production code, supporting model governance, and establishing controls for AI-assisted workflows. The role partners with cybersecurity, engineering, data, and business stakeholders while ensuring solutions are secure, auditable, interpretable, resilient, and aligned with regulatory expectations.

Leads data science and machine learning initiatives addressing cybersecurity and technology risk challenges. Responsibilities include exploratory data analysis, statistical and deep learning model development, feature engineering, secure production coding, model governance, AI workflow guardrails, and stakeholder communication. The role also supports security control effectiveness, production model monitoring, and responsible deployment of AI solutions using enterprise-authorized capabilities.

Supports data collection, cleaning, validation, analysis, visualization, reporting, and basic predictive modeling. The role collaborates with business, data engineering, and technical teams to translate requirements into analytical solutions, communicate insights, improve data quality, and document processes. It uses SQL, Python, Power BI, Excel, and Agile tools while developing experience with statistical methods, cloud data platforms, governance, and emerging AI technologies.

Develop valuation and forecasting models, including customer lifetime value, net present value, and offer economics, to guide marketing investments. Analyze customer, offer, channel, and marketing data; perform data preparation, exploratory analysis, modeling, validation, experimentation, and performance monitoring. Partner with marketing, finance, and data teams to communicate insights and support data-driven recommendations.

Build and deploy predictive, forecasting, pricing, promotion, and machine learning models using large-scale retail data. Apply statistical modeling, causal inference, and experimentation to support demand forecasting, inventory planning, merchandising, and supply chain decisions. Partner with Product, Engineering, Analytics, and Business teams to define requirements, evaluate model performance, and translate complex findings into actionable recommendations and production-ready solutions.

Develop end-to-end AI and machine learning solutions for Product Safety, partnering with business stakeholders to identify opportunities, train and validate models, prototype generative and agentic AI solutions, and deploy solutions with MLOps in cloud environments. The role also involves statistical analysis, reporting, trend modeling, data testing, and communicating insights and recommendations to leadership and customers.

Develop machine learning models supporting credit underwriting and business card strategies. Partner with data scientists, software engineers, product managers, and business stakeholders to design, train, validate, evaluate, backtest, and implement models. Analyze large numeric and textual datasets using Python, AWS, Spark, H2O, and related technologies, translating complex findings into business outcomes.

Lead AI, machine learning, and data science workstreams from methodology through production delivery. Develop scalable, maintainable analytical code, explore new methods, influence product decisions, and collaborate with engineering, product, business, and data teams. Translate technical tradeoffs into actionable recommendations, present findings to technical and business audiences, and deliver measurable business impact while managing risks and maintaining methodological quality.

Build predictive failure models, real-time anomaly detection systems, diagnostic tools, and operational dashboards for spacecraft manufacturing, testing, and integration. Apply statistical modeling, causal inference, reliability analysis, and data mining to identify root causes, bottlenecks, quality issues, supplier risks, and process improvements. Develop reproducible Python and SQL analyses that support engineering decisions and mission reliability.

Build predictive models, anomaly detection systems, diagnostic tools, dashboards, and statistical quality-monitoring solutions for spacecraft manufacturing and testing. Analyze telemetry, production, supplier, and reliability data to identify failures, bottlenecks, delays, and root causes. Develop reproducible Python and SQL analyses and communicate actionable findings to engineering and operations stakeholders.

Perform quantitative analysis, data mining, and statistical modeling to generate actionable product and business insights. Design and track core metrics, build dashboards and visuals, collaborate with cross-functional teams, use AI tools to scale analyses, and drive projects to completion to inform product decisions.

Supports Mastercard’s AI Ops team by applying data science, machine learning, and AI agent techniques to detect anomalies, predict operational impact, initiate remediation, and create dashboards, reports, and alerts. Interns collaborate with stakeholders to generate insights for application monitoring, troubleshooting, and planning while gaining mentorship and professional development experience.

Support model risk governance by maintaining policies, documentation, inventories, validation plans, training, and stakeholder communications. Develop statistical programs and analytical solutions using R, Python, SQL, Hive, Spark, and cloud platforms to analyze large datasets and support credit risk and business intelligence consulting engagements.

Lead people-analytics projects: analyze large HR datasets using statistical methods and ML (SAS/Python/R), build models and visualizations, deliver insights to stakeholders, and partner with data engineers and architects to operationalize solutions.

As a Senior Data Scientist, the role involves leveraging data analysis, experimentation, and statistical methods to influence business decisions and enhance Gusto's product performance, while mentoring team members.

Supports Mastercard’s ONE Data Strategy team by analyzing operational data, developing anomaly detection and forecasting solutions, building dashboards, and translating complex datasets into business recommendations. The intern may contribute to machine learning model development, deployment, telemetry analysis, data engineering, and lakehouse-oriented data platform initiatives while receiving mentorship and professional development.

Develop machine learning and statistical models across large numeric and textual datasets, from design through implementation. Collaborate with data scientists, software engineers, and product managers to deliver customer-focused products. Analyze data, validate and backtest models, apply techniques such as classification, clustering, sentiment analysis, time series, and deep learning, and translate technical findings into business goals.

Analyze global operations and finance data, build predictive and simulation models, optimize supply chain processes, and develop KPI dashboards. The role forecasts demand, inventory, and procurement needs; assesses supply chain risks; improves logistics and supplier performance; maintains data repositories; and communicates actionable insights to procurement, finance, logistics, manufacturing, and other cross-functional stakeholders.