Top Data Science Jobs

Conduct economic research using proprietary banking and public datasets. Build analyses with big-data programming languages and cloud platforms, develop visual analytics, conduct literature reviews, and draft and edit reports. Collaborate with research leaders and academic fellows while applying quantitative and causal-inference methods to household finance and applied microeconomic topics.

Develops advanced analytics and machine learning models using structured and unstructured data to generate actionable business insights. Responsibilities include defining analytics requirements, selecting modeling methodologies, integrating models into business processes, measuring performance, enhancing analytics solutions, creating visualizations, and communicating findings to stakeholders. The role requires collaboration with application developers and independent judgment in solving technical and business problems.

Staff Data Scientist partnering with Growth, Marketing, Product, and Engineering teams to build data science solutions that drive customer acquisition, engagement, and retention. Responsibilities include predictive modeling, personalization, recommendation systems, LLM applications, incentive optimization, causal inference, marketing impact measurement, technical mentorship, and strategic leadership. The role requires deploying production machine learning systems and communicating analytical insights to senior stakeholders.

Lead payments-focused data science work: build foundational datasets, metrics, and scalable analytics; analyze customer and transaction behavior (including onchain data); conduct strategic analyses to identify product and revenue opportunities; build reporting, dashboards, and automation; and influence product and business strategy through data storytelling to senior stakeholders.

Build and operate secure, scalable AI/ML platforms across hybrid and on-premises environments. Develop data pipelines, MLOps workflows, Kubernetes deployments, CI/CD and GitOps automation, observability, infrastructure-as-code, and secure container supply chains. Support model serving, generative AI, and retrieval-augmented generation while troubleshooting distributed systems, improving reliability, and contributing to architecture, standards, documentation, incident response, and technical mentorship.

Leads data science and machine learning initiatives for payment-network applications, including fraud, risk, merchant acceptance, intelligence, and monitoring. Partners with data scientists, engineers, and product managers to deliver customer-focused products. Builds models through design, training, evaluation, validation, and implementation while translating technical findings into business outcomes and developing team talent.

Leads AI/ML and data science products that improve engineering productivity. Translates ambiguous business challenges into scalable, production-grade solutions, shapes scientific direction, ensures methodological quality, mentors scientists, develops reusable frameworks, manages risks and delivery, and collaborates across engineering, data, software, and business teams. Communicates technical tradeoffs to stakeholders and leaders while driving measurable enterprise impact.

Supports data scientists with data exploration, cleaning, analysis, model development, validation, visualization, and prototypes. Uses Python and SQL while learning machine learning, generative AI, cloud data platforms, and production data science practices. Participates in reviews, documentation, presentations, and collaborative projects across multiple co-op terms, progressively increasing responsibility under mentorship.

Lead experimentation, product analytics, and reporting for Owners and Sellers. Conduct exploratory, descriptive, statistical, and predictive analyses; design multivariate tests; define success metrics; and translate consumer behavior insights into product and business strategy. Build AI-enabled self-service reporting, improve tracking and data quality, and develop scalable ETL pipelines while partnering with Product, Engineering, Finance, Marketing, and Business Intelligence teams.

Early-career data scientist supporting sector investment teams: deliver data pulls, analyses, trackers, dashboards, anomaly investigations, and reusable Python/SQL tooling. Use time-series methods, alternative data, and LLMs to accelerate workflows and build commercial judgment with investors.

Own marketing measurement strategy for Paid Social, Programmatic, and Paid App channels. Design experimentation, incrementality, attribution, and marketing mix modeling frameworks; analyze data with SQL, Python, and Tableau; identify growth opportunities; and translate findings into recommendations for senior leadership and multimillion-dollar investment decisions. Partner cross-functionally to implement scalable measurement solutions, establish analytics standards, influence marketing ROI practices, and mentor team members.

Leads the design, development, and deployment of scalable AI, generative AI, and machine learning solutions. Oversees data infrastructure, integration, data quality, and advanced analytics while managing client engagements and executive relationships. Drives business development, strategic direction, innovation, and AI-driven automation. Mentors teams, develops future leaders, and fosters collaboration, integrity, and client satisfaction across complex projects.

Leads AI and GenAI data science workstreams for client engagements. Develops machine learning, deep learning, and NLP models; applies statistical and predictive modeling; designs scalable data science workflows and pipelines; evaluates model performance; communicates insights through visualizations and executive narratives; coaches teams; and supports responsible AI governance.

Leads Mastercard’s Open Finance data science strategy and global organization. Oversees production-grade AI and machine learning solutions for transaction categorization, entity extraction, fraud detection, credit risk, propensity modeling, personalization, and financial insights. Partners with Product, Engineering, Sales, Risk, Marketing, Legal, and Compliance to commercialize scalable solutions, establish MLOps and governance practices, ensure responsible AI, and grow high-performing data science teams.

Leads Mastercard’s Open Finance data science strategy, products, and global organization. Oversees production machine learning and AI capabilities for transaction categorization, entity extraction, fraud detection, credit risk, personalization, and predictive analytics. Partners with Product, Engineering, Sales, Risk, Marketing, Legal, and Compliance to commercialize scalable, explainable, and responsibly governed solutions. Builds talent, establishes operating standards, drives MLOps and model governance, and advises executives and customers on AI applications.

Performs advanced analysis and data mining on large structured and unstructured datasets to generate business insights. Develops machine learning algorithms and statistical models to predict outcomes and recommend actions. Conducts analytical experiments for product and process optimization, presents findings through visualization and data storytelling, and collaborates with data architects, analysts, engineers, and visualization experts.

Analyze complex datasets to deliver actionable insights for production, resource allocation, and personnel decisions. Develop Tableau dashboards and visualizations, preprocess and clean structured and unstructured data using Python or R, and analyze time-series data. Collaborate with stakeholders, ensure data quality, document methodologies, and provide recommendations. The role requires an active TS/SCI with Polygraph clearance and is onsite in Northern Virginia.

Build and deploy machine learning models for personal loan acquisition, including underwriting, pricing, verification, list selection, and campaign optimization. Analyze large-scale numeric and textual datasets using Python, Spark, AWS, H2O, and related tools. Collaborate with data scientists, engineers, and product managers, validate and backtest models, and translate statistical insights into business outcomes.

Build and implement machine learning models for personal loan acquisition, including customer selection, campaign optimization, underwriting, pricing, and verification. Partner with data scientists, engineers, and product managers; analyze large-scale numeric and textual datasets using Python, Spark, AWS, and related tools; validate and backtest models; and translate statistical insights into customer-focused business outcomes.

Develop and maintain data-driven reporting, applications, data tables, and analytical solutions for claims and returns operations. The role uses SAS, Power BI, Microsoft Power Platform, and Snowflake-related data environments to fulfill reporting requests, troubleshoot issues, analyze business needs, and communicate recommendations. Responsibilities include requirements gathering, cross-functional collaboration, ad hoc analysis, and leading moderately complex projects using Lean and Six Sigma methodologies.

Develop predictive models and advanced analytics for credit risk, fraud, insurance, and marketing use cases. Lead client analytics engagements from design through implementation and presentation, analyze large-scale datasets, build scalable analytical frameworks, and translate findings into business recommendations. Collaborate with clients, sales teams, and internal stakeholders while supporting proposals and analytics consulting.

Embedded Data Scientist for Superhuman Mail, partnering with product, growth, engineering, and design teams to guide roadmap decisions. Responsibilities include defining product metrics, designing experiments, applying causal inference and machine learning, measuring AI feature quality, analyzing activation and retention, identifying growth and conversion drivers, and communicating insights to stakeholders. The role requires strong Python, SQL, experimentation, statistics, and cross-functional influence skills.

Analyzes complex data sets, develops Python-based data solutions and pipelines, and applies advanced analytics to support client strategy and business performance. Designs dashboards and visualizations, collaborates with cross-functional teams, manages stakeholders, improves data quality, and builds client relationships. The role also involves solving complex business challenges, identifying industry trends, and delivering high-quality work within PwC’s Risk & Regulatory practice.

Build and operate uplift and machine learning models that optimize personalized retention bonuses. Responsibilities include feature pipeline development, model deployment, monitoring, experimentation, causal analysis, large-scale behavioral data analysis, and communicating business impact to Marketing stakeholders. The role also involves weekly model operations, cross-functional collaboration, documentation, code review, and developing reusable modeling practices.

Develop and scale geospatial analytics using GEOINT, OSINT, SIGINT, and foundational intelligence data. Responsibilities include ingesting and spatially processing diverse datasets, conducting pattern-of-life and targeting analytics, correlating data at scale in an on-premises HPC environment, creating automation workflows, and collaborating with customers, analysts, developers, and subject matter experts to deliver actionable intelligence products.