Top Senior Data Science Jobs

Lead development of large-scale recommender and personalization systems using transformers, LLMs, reinforcement learning, sequence modeling, and multimodal learning. Build and evaluate foundation models and low-latency production systems using customer interaction data. Collaborate with engineering and product teams, translate research into practical applications, and support innovation in generative recommendations, conversational systems, and cross-domain personalization while developing team talent.

Build enterprise Generative AI solutions for Commercial Banking using multi-agent systems, advanced RAG, context engineering, tool-calling frameworks, and machine learning. Partner with data scientists, ML engineers, and product managers to develop, evaluate, validate, fine-tune, and deploy AI pipelines over large numeric and textual datasets. Translate complex technical work into business outcomes while researching emerging AI methods.

Leads a US Card data science team developing machine learning solutions for customer behavior prediction and valuation forecasting. Partners with data scientists, engineers, product managers, and business stakeholders; builds models through design, training, validation, evaluation, and implementation; analyzes large-scale numeric and textual data; translates technical findings into business outcomes; and develops team talent.

Develop enterprise Generative AI solutions for Commercial Banking using multi-agent systems, advanced RAG, context engineering, and tool-calling frameworks. Partner with data scientists, machine learning engineers, and product managers to build, evaluate, validate, fine-tune, and deploy AI pipelines across numeric and textual data. Apply statistical modeling, machine learning, deep learning, NLP, information retrieval, and cloud technologies while translating complex technical work into business outcomes.

Leads enterprise AI solution validation and certification, making risk-based release decisions and overseeing post-deployment performance, risk, and safety monitoring. Partners with solution owners and data teams to enforce AI governance requirements, manage remediation, and provide authoritative guidance on validation standards. Directs advanced analytics, data mining, and machine learning initiatives while providing enterprise-wide program leadership and executive-level influence.

Leads payer healthcare data science and operations consulting initiatives, using business analytics to improve payer processes and performance. Manages teams, client relationships, project resources, strategic planning, process optimization, quality standards, and operational transformation. Coaches junior staff, promotes innovative technologies, and identifies opportunities to enhance operational excellence and business outcomes.

Develops and deploys AI/ML capabilities for tactical edge environments. Responsibilities include sourcing and labeling data, training and evaluating computer vision and LLM models, optimizing models for embedded hardware, building Docker-based offline pipelines, troubleshooting Linux systems and drivers, analyzing RF and communications data, evaluating open-source solutions, supporting customers, and documenting model performance.

Leads enterprise Responsible AI governance and oversight, including governance forums, AI solution reviews, risk assessment, stakeholder engagement, executive communications, training processes, and academic partnerships. Directs advanced analytics and data science projects involving large structured and unstructured datasets, provides technical leadership, and partners with architects, analysts, engineers, and visualization experts to deliver data-driven solutions.

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.

Develop and deploy machine learning solutions for enterprise risk and anti-money-laundering innovation. Partner with data scientists, engineers, and product managers to build models using structured and textual data, including NLP, graph modeling, deep learning, and Generative AI. Lead projects through design, training, validation, evaluation, and implementation while translating technical findings into business outcomes.

Leads fraud-risk analytics and strategy across the account lifecycle, analyzing fraud trends, account quality, performance metrics, profitability drivers, and operational processes. Directs complex projects from analysis through implementation and monitoring, identifies fraud risk drivers, and delivers recommendations to senior leadership. Collaborates cross-functionally, builds consensus, and communicates quantitative insights to executives and stakeholders using statistical methods, SAS, SQL, and spreadsheet tools.

Lead complex data science and AI initiatives focused on healthcare affordability, medical cost management, and operational efficiency. Develop and deploy machine learning, causal inference, optimization, and AI solutions using healthcare claims and enterprise data. Guide architecture, governance, validation, and measurement of production models; mentor data scientists; establish technical direction; and communicate insights and business value to actuarial, clinical, business, technology, and executive stakeholders.

Lead the design, development, and operation of scalable data pipelines, trusted data products, metrics, and feature-ready datasets. Establish data quality, governance, orchestration, testing, observability, and engineering standards using Python, PySpark, SQL, dbt, Airflow, and Databricks. Partner with analytics, product, and engineering teams to support AI adoption and productivity measurement, while mentoring a team of two and driving delivery through ambiguous, cross-functional initiatives.

Leads consumer data science strategy and high-impact analytical initiatives, including experimentation, causal inference, measurement frameworks, and AI-assisted analytics. Partners with Product, Engineering, and executives to shape roadmaps, define metrics, communicate recommendations, and prioritize business opportunities. Mentors data scientists, establishes technical standards, and develops scalable self-service analytics capabilities using modern data and AI tools.

Develops data science and machine learning solutions for auto finance products. Analyzes digital customer behavior, builds predictive models, evaluates A/B experiments, and creates measurement frameworks and KPIs to improve conversion and engagement. Partners with Product, Technology, Design, Risk, and business leaders to translate complex questions into actionable insights, communicate recommendations, and support strategic decisions. May also build conversational AI solutions and integrate machine learning services into applications.

Own analytical integrity for a payments observability and AI platform. Responsibilities include analyzing transactional data, validating metrics and model behavior, back-testing anomaly detection and forecasting models, monitoring data and model drift, triaging analytical defects, creating executive-ready visualizations, and partnering with product and engineering teams on requirements and prioritization.

Own product analytics for a healthcare platform by translating business questions into research plans, uncovering insights, modeling feature impacts, defining success criteria, and leading A/B testing. Partner with Product, Data Engineering, Finance, Clinical, and Operations teams to influence roadmap decisions, improve provider experience and quality, and optimize marketplace performance.

Develop and deploy large-scale machine learning and personalization models for web and mobile customer experiences. Partner with data scientists, engineers, and product managers; analyze massive numeric and textual datasets; and manage model design, training, evaluation, validation, and implementation. Research advanced methods including foundation models, reinforcement learning, causal inference, transformer architectures, and recommender systems while translating technical findings into business outcomes.

Develop and deploy high-scale machine learning and personalization models for Capital One’s web and mobile experiences. Responsibilities include researching recommendation systems, foundation models, reinforcement learning, causal inference, and transformer architectures; analyzing large datasets; building models through training, evaluation, validation, and implementation; and collaborating with data scientists, engineers, and product managers.

Build and deploy machine learning models and scoring platforms for credit decisioning and valuation. Analyze large-scale financial and textual datasets using statistical modeling, cloud technologies, and open-source tools. Responsibilities include model design, training, validation, implementation, analytical tool development, alternative data analysis, and collaboration with software engineering and product teams to deliver scalable credit products.

Leads data science initiatives for commercial banking by developing and validating machine learning models, conducting statistical analysis and back-testing, modernizing data infrastructure, and building analytical tools for risk, underwriting, portfolio management, and client-focused products.

Build and deploy machine learning and statistical models across the credit card lifecycle, including marketing, acquisitions, underwriting, and fraud prevention. Partner with data scientists, software engineers, and product managers; analyze large-scale numeric and textual datasets; conduct model design, training, evaluation, validation, and implementation; and translate complex analyses into business outcomes.

Leads a small data science team and cross-functional initiatives transforming bank claims management through machine learning and agentic workflows. Responsibilities include data exploration, model development, evaluation, validation, deployment, roadmap development, stakeholder influence, and establishing datasets, tools, and evaluation protocols for enterprise AI. The role remains hands-on while providing strategic oversight and developing team talent.

Develop and maintain Java and React applications using Spring Boot, microservices, AWS ECS, and DevOps practices. Build scalable cloud applications, data pipelines, integrations, and tooling; optimize performance and reliability; troubleshoot production issues; and collaborate with designers, product owners, engineers, and business stakeholders. The role also involves data-driven insights, Agile delivery, CI/CD, code reviews, and exploring AI-enabled user interface solutions.