Top Senior Data Science Jobs

Lead real-world evidence studies for pharmaceutical clients using complex clinical, molecular, claims, EHR, and registry data. Design observational analyses, derive endpoints, apply survival analysis and causal inference methods, interpret results, and communicate findings to technical and non-technical stakeholders. Develop reusable analytical code and standards while incorporating LLMs and agentic workflows into research processes. Collaborate cross-functionally to improve data quality, products, and RWE methodologies, with a focus on oncology and clinical development.

Lead end-to-end data science and machine learning initiatives, from identifying business use cases and querying data through prototyping, evaluation, deployment, and monitoring. Partner with Product, Engineering, and Machine Learning Engineering teams across areas such as risk, insurance, and payments. Develop predictive models, statistical methods, deep learning, and LLM solutions while contributing reusable data assets, tooling, MLOps strategy, technical reviews, and data-informed business decisions.

Own product-led growth analytics across activation, engagement, conversion, monetization, and retention. Lead experimentation, statistical analysis, causal inference, metric development, and proactive opportunity discovery. Build scalable analytical systems, monitoring frameworks, semantic models, and AI-powered tools. Partner with Product, Engineering, and leadership to shape product bets, investment decisions, and roadmap priorities. Work autonomously in a remote-first environment and communicate complex findings through clear, actionable recommendations.

Develops advanced statistical methods, selects machine learning algorithms, and supports enterprise data strategy for a government customer. Responsibilities include processing and optimizing large datasets, applying machine learning, statistical modeling, forecasting, and geospatial analytics, and developing AI/ML models for object detection, data triage, search optimization, inference, automated discovery, and decision-making.

Develop advanced machine learning, data mining techniques, predictive models, and analytical frameworks to identify and mitigate cybersecurity threats. Integrate cybersecurity data sources and sensor technologies, collaborate with cross-functional stakeholders, and deliver actionable insights supporting client and mission objectives. The role requires substantial cybersecurity and data science experience, leadership capability, strong communication skills, a Top Secret clearance, and relevant education or certifications.

Architects and deploys scalable data science solutions for GEOINT and national security missions. Responsibilities include integrating multi-INT data, developing Python pipelines, applying machine learning and statistical methods, creating visualizations and dashboards, conducting geospatial analysis, and translating customer intelligence requirements into production capabilities. The role provides technical leadership, mentors data scientists, supports intelligence operations, and produces ABI tradecraft products aligned with NGA and DNI standards.

Develop predictive models, algorithms, quantitative analyses, and visualizations from structured and unstructured data. Collaborate with clients to understand data needs, lead solution development, apply machine learning and NLP techniques, and translate complex information into actionable insights. The role also involves guiding teammates and advising clients across public and private sector challenges.

Supports defensive cyberspace operations through advanced statistical analysis, machine learning model development, operational research, threat detection, risk scoring, data visualization, and analytical reporting. Responsibilities include data preparation, quality assurance, collaboration with data engineers on model deployment, and documentation of analytical methods. The role supports Department of War Cyber Defense Command at Fort Meade and requires U.S. citizenship and a DoD Top Secret clearance with SCI eligibility.

Build and operate production calibration and image-processing pipelines for multispectral infrared satellite imagery. Translate calibration algorithms into reliable software with testing, monitoring, alerting, diagnostics, CI/CD, and observability. Analyze pre-launch and on-orbit data, validate calibration coefficients, manage uncertainty budgets, and support calibration campaigns for FireSat and SBEM missions. Collaborate with scientists, instrument engineers, and product teams to ensure radiometric integrity and scientific quality of data products.

Develop and productionize algorithms that transform multispectral infrared satellite imagery into derived geospatial data products. Build scalable Python-based processing pipelines, validate scientific quality, document product designs, and integrate operational services for customer-facing applications. Collaborate with scientists, software and instrument engineers, product owners, and customers across wildfire, weather, and environmental monitoring missions. The hybrid role requires three days weekly onsite in Denver, Colorado.

Supports data science teams by cleaning and preparing structured and unstructured datasets, automating ETL workflows, and developing scalable data pipelines. Uses Python, SQL, big-data frameworks, cloud services, and dashboarding tools to support analytics, modeling, LLM/NLP workflows, and operational data products. Maintains documentation, assists with data governance and visualization, and contributes to mission-focused intelligence solutions in a highly sensitive environment.

Leads enterprise data and analytics, advising government leadership on research methods, study design, program evaluation, and decision support. Oversees data scientists and engineers, establishes analytical and governance standards, directs advanced statistical and predictive analyses, develops dashboards and readiness metrics, and ensures reproducible, compliant methodologies. Requires strong Python or R, SQL, supervisory, research, and stakeholder communication skills, plus a current Top Secret clearance and ability to obtain Top Secret/SCI clearance.

Lead advanced GEOINT exploitation across EO, IR, SAR, multispectral, and specialized sensor collections. Develop machine learning, statistical, and data-fusion models for detection, classification, change analysis, and intelligence workflows. Conduct image science and sensor performance analyses, integrate AI capabilities, establish analytic standards, mentor technical staff, and deliver mission-critical products and briefings to government and intelligence customers.

Leverage data to support Slack growth initiatives, identify customer behavior trends, forecast performance, and uncover growth opportunities. Partner with Growth, Data Engineering, Strategy, Go-To-Market, Product, Design, and Engineering teams. Build analytical frameworks, core data models, self-service tools, and reports, while communicating insights and recommendations to senior leadership.

Own Workhelix’s analytical engine end to end, including customer data integration, ETL, data modeling, AI opportunity assessments, predictive analyses, visualizations, and production deployment. Build Python and SQL systems running in cloud infrastructure, develop customer-specific analyses, create internal analytical tools, and shape the data science roadmap. Partner with engineers, strategists, executives, and customers while setting standards for analytical rigor and translating research into practical business decisions.

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.