Top Senior Data Science Jobs

Analyze complex client problems using statistical modeling, exploratory analysis, data visualization, and machine learning. Develop AI and GenAI solutions, data workflows, pipelines, models, and quality checks that generate actionable business insights. Communate recommendations through documentation and visual storytelling, manage workstreams, collaborate with clients, and mentor junior team members.

Leads people analytics and quantitative insights initiatives using statistical analysis, machine learning, data modeling, and visualization. Analyzes complex workforce datasets, develops predictive algorithms and dashboards, identifies optimization opportunities, and communicates actionable recommendations to technical and business stakeholders. The role manages multiple analytical projects and partners with data architects, engineers, analysts, and visualization experts. It requires a master’s degree and typically five or more years of relevant experience.

Lead client-facing analytics and AI consulting engagements from solution design through model development, evaluation, deployment readiness, and monitoring. Apply machine learning, statistical modeling, generative AI, LLMs, MLOps, and responsible AI practices. Translate client needs into scalable solutions, communicate insights to technical and business stakeholders, identify commercial opportunities, contribute to research and innovation, and mentor junior colleagues.

Leads teams in designing and deploying scalable AI and machine learning solutions for consulting clients. Responsibilities include data wrangling, complex analysis, data modeling, AI system implementation, data pipeline and infrastructure oversight, deep learning, neural networks, data quality validation, and stakeholder collaboration. The manager coaches team members, manages performance, supports innovation, and applies Python, Java, and cloud technologies to build production-ready AI applications.

Build and deploy predictive models, recommendation systems, LLM applications, incentive optimization frameworks, and causal inference models for customer growth. Partner cross-functionally to improve acquisition, engagement, retention, and expansion. Measure marketing impact, guide investment decisions, develop AI analytics and decision systems, and provide technical leadership and mentorship across data science initiatives.

Own product analytics and data science for a business-critical product area. Develop production-ready churn, resurrection, and targeting models; define metrics; analyze experiments; apply causal inference; and deliver actionable recommendations with quantified business opportunities. Partner closely with Product, Engineering, Design, and Growth teams while contributing to experimentation methodology, standards, and tooling.

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.

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.

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.

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.

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.

Leads data science initiatives using machine learning, statistical modeling, and large-scale data analysis. Partners with software engineers and product managers to develop customer-focused products, builds and validates models across the full development lifecycle, translates technical findings into business outcomes, evaluates emerging technologies, and develops data science talent. Requires substantial experience with programming, machine learning, relational databases, cloud computing, and people management.

Develop Generative AI solutions for financial services using Python, PyTorch, LangChain, AWS, Spark, vector databases, multi-agent systems, and advanced RAG pipelines. Build, fine-tune, evaluate, validate, and deploy machine learning models across large numeric and textual datasets. Collaborate with data scientists, machine learning engineers, and product managers while translating technical findings into business outcomes.

Designs and optimizes AI and machine learning solutions, transforms data into insights, and develops production AI systems. Responsibilities include building data infrastructure, speech recognition and NLP tools, and reusable evaluation and monitoring capabilities. The role independently tests AI systems for performance, robustness, bias, explainability, and failure modes; evaluates technical controls; documents findings; communicates recommendations to clients; and mentors junior team members.

Leads the design, development, testing, and evaluation of AI and machine learning solutions. Directs data analysis, modeling, infrastructure, monitoring, and integration efforts while establishing standards for AI robustness, bias, explainability, failure modes, and technical controls. Manages large projects, advises clients, coaches teams, and develops reusable evaluation tools and evidence workflows for responsible AI deployment.

Lead applied AI and data science work for recommendation and personalization systems. Partner with data scientists, engineers, and product managers to deliver customer-facing products; develop, train, evaluate, validate, and implement machine learning models; analyze large-scale numeric and textual datasets; apply statistical modeling, deep learning, causal inference, reinforcement learning, and recommender-system techniques; and communicate technical findings to business stakeholders while developing team talent.

Develops data integration workflows, dashboards, statistical and predictive models for biotherapeutics process development. Analyzes complex scientific and process data, identifies insights and anomalies, troubleshoots data and analysis issues, and communicates findings to technical teams. The role also supports project deliverables, external collaborations, process improvements, and training junior staff on data tools, analysis methods, and visualization practices.

Own recurring reporting and ad hoc analysis of consumer deposit account openings across checking, savings, and CDs. Develop automated performance views, identify business drivers, ensure data quality and reproducibility, and present concise insights and recommendations to senior executives. Partner with business, finance, product, marketing, and data teams to validate results and connect trends to business activity.

Leads end-to-end data science projects, including problem framing, data exploration, feature engineering, model development, evaluation, deployment, and communication. Applies statistical analysis, experimentation, and machine learning to business problems across marketing, underwriting, actuarial, operations, and fraud prevention. Partners with engineering and cross-functional stakeholders to deliver scalable analytical solutions, actionable insights, process improvements, and enhanced model performance.

Develop and govern machine learning valuation models for credit line increase and customer management programs. Responsibilities include data analysis, model design, training, validation, deployment, performance monitoring, backtesting, and stakeholder collaboration. The role partners with data scientists, software engineers, and product managers while using Python, cloud computing, big data, and statistical modeling techniques to support credit program decisions.

Leads data science projects using large structured and unstructured datasets to generate business insights. Directs data gathering, processing, mining, and advanced machine learning algorithm development. Conducts analytical experiments, recommends product and process improvements, and presents findings through visualization and data storytelling. Partners with architects, analysts, engineers, and visualization experts to deliver data-driven business solutions.

Lead a data science team delivering analytics, machine learning, and AI solutions for wealth management. Partner with product, strategy, and operations stakeholders to frame problems, define success metrics, analyze client and portfolio outcomes, operationalize experimentation, and communicate executive-ready recommendations. Coach data scientists, develop scalable analytical capabilities, and identify high-value AI and agentic use cases that improve decision quality, client outcomes, and operational efficiency.

Leads product analytics for small business lending by framing business questions, building and validating datasets, developing KPI and reporting frameworks, conducting statistical and predictive analyses, and translating findings into executive-ready recommendations. Partners with product, lending strategy, risk, finance, technology, and operations stakeholders to improve measurement, automation, data governance, customer understanding, and lending-product performance.

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.