Top Senior Data Science Jobs

Leads data science initiatives by exploring, cleaning, analyzing, and visualizing structured and unstructured data. Develops predictive models, quantitative analyses, machine learning and NLP solutions, and algorithms that inform client decisions. Guides teammates, leads complex solution development, and works with clients to translate data into actionable insights. May support software integration, testing, scripting, ETL, APIs, and advanced analytics using Python, R, SQL, and related tools.

Develop and optimize data-driven marketing strategies for credit card products. Responsibilities include campaign analytics, modeling, experimentation, funnel and ROI analysis, direct mail campaign execution, KPI definition, dashboard development, data pipeline validation, and performance monitoring. The role partners with marketing, product, business leaders, and external stakeholders to identify growth opportunities, communicate insights, and drive profitable member acquisition and retention.

Leads development of intelligence-focused analytics products, integrating structured and unstructured data into analytic workflows. Performs quantitative and qualitative intelligence analysis supporting operations and exercises, provides onsite subject-matter expertise during command exercises, and supports senior leaders through executive briefings and report writing. The role requires strong intelligence reporting skills, Indo-Pacific national security expertise, and an active Secret clearance with eligibility for Top Secret.

Lead the design, development, deployment, monitoring, and improvement of interconnected customer lifetime value models covering conversion, retention, premiums, and claim losses. Serve as a hands-on technical leader, guide modeling strategy, evaluate experiments and business impact, partner with engineering and business teams, support production ML systems, establish technical standards, and coach other data scientists.

Leads advanced GEOINT exploitation across EO, IR, SAR, multispectral, and specialized sensors. Develops machine-learning and statistical models for detection, classification, change analysis, and data fusion; evaluates sensor performance; designs reproducible data workflows; integrates AI capabilities; delivers technical briefings and intelligence products; establishes analytic standards; mentors staff; and collaborates with government and intelligence stakeholders.

Lead homeowners insurance loss-cost modeling, including frequency, severity, demand, retention, conversion, and aggregation models. Design and deploy predictive models using Python, SQL, and Airflow-based workflows, ensuring testing, validation, reproducibility, and maintainability. Partner with product, underwriting, and engineering teams to translate actuarial insights into pricing and portfolio decisions while developing reusable analytical tooling and supporting team standards.

Analyzes large datasets using data science, machine learning, predictive modeling, and big data technologies. Designs approaches for data handling, search, retention, storage, and processing; develops software to improve data searches; validates data quality and processes; and reports actionable insights. The role emphasizes scientific analysis over visualization and requires an active Top Secret/SCI clearance with potential Counterintelligence Polygraph eligibility.

Develops AI and automation solutions for IT asset management, including stakeholder agents, anomaly detection, data quality improvements, predictive asset insights, data modeling, dashboards, and regulatory reporting. Leads resiliency review sessions, recommends actions within risk tolerances, translates business needs into data-rich outputs, and presents findings to diverse audiences. The role also supports infrastructure compliance, process improvement, project planning, and production support.

The Data Scientist will turn ambiguous B2B and go-to-market questions into actionable insights for strategy, monetization, product adoption, customer segmentation, field deployment, and organizational design. Responsibilities include analyzing product, customer, sales, revenue, and market data; partnering with cross-functional leaders; communicating recommendations to executives; applying AI tools; and helping establish the operating model for an emerging B2B data science team.

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.

Build and maintain data science models and quantitative measurement frameworks to guide infrastructure decisions (chargers, parking, depots). Analyze infra efficiency and identify improvements. Collaborate cross-functionally with Supply Operations, Product, and Engineering to incorporate business context into model-driven decisions supporting expansion to new territories and cities.

Develop data-driven decision analyses for Department of Warfare clients. Responsibilities include collecting, validating, and analyzing data; creating predictive models, trend analyses, dashboards, and business intelligence solutions; documenting data quality and findings; developing data frameworks and pipelines; automating processes; and communicating insights to government stakeholders. The role requires knowledge of DoW acquisition, financial, and PPBE processes, along with an active Secret clearance and five or more years of experience.

Performs data science for a Joint Interagency Task Force supporting counter-drone capabilities and national airspace security. Develops data taxonomies, analyzes data trends, provides data modeling for algorithms, and creates data-sharing mechanisms with external stakeholders. Requires systems and cloud administration experience, preferably with Microsoft Azure Government, and may involve travel to field locations. The role is onsite in Alexandria, Virginia and requires U.S. citizenship and an active Secret clearance.

Lead end-to-end data science projects to build, validate, and deploy statistical and machine learning models. Drive experimentation, causal analysis, and decision-support analytics for product and business stakeholders, mentor junior team members, and partner with engineering to productionize models and measurement frameworks.

Build knowledge bases and extraction pipelines for generative AI/ML research. Develop metadata, annotation, provenance, and reproducible dataset workflows; analyze experimental, simulation, and evaluation data; create dashboards and reports; validate structured knowledge; and support research documentation and coordination with academic subcontractors.

Leads development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Responsibilities include feature engineering, forecasting, anomaly detection, data integration, ETL, visualization, algorithm evaluation, simulation, dataset creation, and stakeholder communication. The role also contributes to mission-focused software applications, APIs, data pipelines, documentation, code reviews, and agile development while requiring an active Top Secret or CBP/DHS suitability clearance.

Develop and deploy advanced machine learning and data science solutions across large numeric and textual datasets. Partner with data scientists, engineers, and product managers to build customer-focused products. Lead model design, training, evaluation, validation, and implementation while researching emerging technologies and translating complex analyses into business outcomes.

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.

Leads experimentation, causal inference, advanced statistical analysis, machine learning, and scalable data-product development. Partners with product, engineering, and research teams to generate insights, identify opportunities, influence executive decisions, and shape product strategy. Owns cross-functional projects, analytics roadmaps, reporting, stakeholder adoption, and methodological standards while translating complex data into actionable business outcomes.

Owns experimentation, business analytics, reporting, and advanced analysis for Realtor.com’s RDC Next platform. The role analyzes consumer and agent behavior, designs A/B and quasi-experiments, builds trusted datasets and dashboards, and delivers recommendations that influence product and business strategy. It also develops AI-enabled analytics workflows using LLMs, RAG, and agentic tools; partners with Product, Engineering, Finance, Marketing, and data teams; mentors analysts; and helps establish analytical standards and scalable data solutions.

Leads Responsible AI data science initiatives by developing and validating AI testing protocols, automating analyses with Python, identifying PII and PHI vulnerabilities, and designing red-team scenarios for conversational AI. The role documents responsible AI practices, supports governance and regulatory compliance, communicates findings to legal, compliance, and technical stakeholders, and mentors colleagues. It requires expertise across traditional machine learning, generative AI, agentic techniques, AI ethics, fairness, and the full AI lifecycle.

Leads product analytics, experimentation, and advanced analyses for consumer content and visualization experiences. Defines success metrics, designs tests, improves tracking and data quality, builds ETL pipelines and AI-enabled self-service reporting, and translates consumer behavior insights into product and business strategy. Partners with Product, Engineering, Finance, and data teams to identify opportunities, measure performance, and influence the product roadmap.

Senior analytics partner leading product experimentation, business analytics, consumer behavior analysis, and performance measurement for content and visualization experiences. Responsibilities include exploratory analysis, multivariate testing, metric definition, data quality improvement, ETL pipeline development, self-service AI-enabled reporting, and translating statistical or predictive insights into product and business strategy. The role collaborates with Product, Engineering, Finance, and data teams to influence roadmap decisions and improve consumer engagement.

Leads CRM marketing analytics for Seller and Owner verticals by developing segmentation, targeting, personalization, customer journey, and campaign measurement strategies. Analyzes consumer behavior to improve engagement, retention, lead generation, cross-selling, and revenue. Builds statistical and machine learning models, integrates CRM data, establishes KPIs, communicates insights to stakeholders, and partners with Marketing, Data Engineering, and IT teams.