Top Senior Data Science Jobs

Develop and implement data science strategies for machine learning applications including lead scoring, churn modeling, pricing, personalization, and fraud detection. Own end-to-end initiatives spanning data analysis, feature engineering, model training, inference, and monitoring. Collaborate with engineering, infrastructure, analytics, and business teams to deliver measurable impact, improve data capabilities, and communicate findings to technical and non-technical stakeholders.

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.

Develops AI/ML models, geospatial analytics, scientific software, APIs, cloud data services, and processing pipelines for NASA Earth science datasets. The role integrates satellite, environmental, and numerical model data using Python, Fortran, GIS, remote sensing, AWS, and Azure. Responsibilities include supporting data archives, collaborating with scientists, maintaining legacy applications, exploring quantum computing applications, and producing technical documentation and scientific publications.

Staff Data Scientist responsible for strengthening measurement quality, consistency, scalability, and business impact. Partners with analytics, data science, product, engineering, and business teams to evaluate causal inference, experimentation, incrementality, attribution, and media mix modeling. Develops methodological frameworks, identifies analytical risks and limitations, provides evidence-based recommendations, and resolves complex measurement issues. Translates advanced analytical findings into practical business solutions while guiding stakeholders and senior leadership.

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.

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.

Develops and deploys predictive and machine learning models, assesses data quality, sources data from SQL and NoSQL systems, and translates business problems into actionable insights. The role creates stakeholder-focused visualizations, supports scalable production deployments, monitors model performance, and mentors junior data scientists. Work supports fraud prevention, threat detection, digital risk management, and operational improvement.

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.

Develop machine learning and statistical models to identify, measure, and manage credit and fraud risk. Partner with stakeholders to translate requirements into scalable solutions, analyze large application and transaction datasets, deploy end-to-end models, and improve risk decision-making. The role requires advanced Python or R and SQL skills, expertise in fraud and underwriting models, model deployment, statistical methods, and mentoring junior data scientists.

Lead data science initiatives supporting Product and Commercial Operations. Analyze growth opportunities, define success metrics, conduct experimentation and causal inference, and deliver actionable insights. Lead initiatives such as experimentation systems and data agents while promoting strong analytical practices. Partner with stakeholders to drive measurable product impact, minimize marketplace losses, and communicate complex findings to executive audiences.

Serves as a client-facing data science and technology consultant for a healthcare data platform. Analyzes large-scale claims, clinical, and unstructured datasets; develops statistical, machine learning, predictive, and prescriptive models; and creates prototypes and scalable analytics solutions. Partners with product, engineering, and client services teams, translates client needs into recommendations, supports onboarding and troubleshooting, and provides technical guidance on AI, cloud analytics, and platform adoption.

Leads the design and development of Walmart Connect’s ads incrementality measurement platform. Owns causal inference methodology, randomized experimentation, uplift modeling, evaluation frameworks, technical roadmaps, and production partnerships. Develops large-scale incrementality models, collaborates with engineering on data infrastructure, presents technical work to leadership, and mentors scientists. Requires deep expertise in causal inference, experiment design, statistical modeling, machine learning, Python, SQL, and distributed data platforms.

Develop and operationalize machine learning and statistical solutions for pharmaceutical development and manufacturing. Responsibilities include predictive modeling, computer vision, exploratory analysis, model validation, deployment, monitoring, software package development, testing, documentation, and model governance. The role translates scientific and business challenges into scalable applications, collaborates with multidisciplinary teams, supports AI/ML projects, and communicates findings to scientific and cross-functional stakeholders.

Develop, train, evaluate, deploy, and monitor machine learning and statistical models using Databricks, Python, SQL, Spark, Delta Lake, and MLflow. Prepare datasets, engineer features, manage model lifecycles, validate data, and support governance practices. Translate client requirements into scalable analytics solutions, analyze trends, and communicate findings to technical and non-technical stakeholders while collaborating across data science, engineering, cloud, security, and business teams.

Develops machine learning models, statistical analyses, predictive solutions, and analytical pipelines, with a focus on geospatial datasets. Responsibilities include exploratory analysis, data visualization, model optimization, feature engineering, GIS analysis, documentation, and presenting findings. The role collaborates with stakeholders on mission-critical projects and requires onsite work at Stennis Space Center while supporting a government customer.

Lead product data science and analytics for Remitly’s high-value sender segment. Analyze customer journeys, funnels, and behavioral trends; design and evaluate experiments; apply statistical and causal inference methods; connect marketing activity to product outcomes; improve instrumentation and data quality; and influence product roadmaps through evidence-based recommendations. Mentor analysts, strengthen experimentation standards, and communicate insights and uncertainty to technical and business stakeholders.

As a Data Scientist, you will leverage data exploration, analysis, and machine learning to provide insights and solutions for clients, using various data sources and tools to address complex challenges.

Develop predictive models, algorithms, quantitative analyses, and visualizations from structured and unstructured data. Extract insights from databases and APIs, apply machine learning and NLP techniques, lead complex solution development, and advise clients on data-driven decisions. The role requires TS/SCI clearance with polygraph and involves Python, SQL, AI engineering, production ML pipelines, and distributed computing frameworks.

Leads the design, development, and maintenance of scalable data pipelines, enterprise relational databases, and data integrations supporting visual analytics. Transforms structured and semi-structured data into actionable insights, improves warehouse and system performance, and establishes database, programming, security, and data-quality standards. The role also integrates open-source, commercial, and government technologies while collaborating with teams on secure data workflows and analytics solutions.

Provides data analytics support to the U.S. Navy’s DPAE Maritime M&S program office. Develops analytics applications, applies data mining, modeling, NLP, and machine learning, and creates data visualizations and reports. Coordinates data calls, evaluates operational deficiencies, supports leadership briefings, manages meetings and action items, and assists with administrative coordination. Requires domestic and international travel, Navy program office experience, Microsoft Office and SharePoint proficiency, and eligibility for a Secret clearance.

Provide advanced data analytics and machine learning support to a U.S. Navy program office. Responsibilities include developing Qlik Sense data models and ETL pipelines, analyzing large unstructured datasets, creating predictive and trend-reporting logic, building geospatial visualizations, optimizing application performance, and designing leadership dashboards. The role also requires presentations, client support, domestic and international travel, and eligibility for a DoD Secret clearance.

Develop and maintain credit risk, repayment, and predictive models powering financial products. Engineer features from transaction data, deploy and monitor production ML models using AWS and Snowflake, analyze model performance and drift, prototype modeling methods, and collaborate with product, risk, and engineering teams. Communicate findings to technical and non-technical stakeholders while applying explainable AI, time-series modeling, and financial machine-learning best practices.