Top Senior Data Science Jobs

Develop predictive models and automated analytic solutions using structured and unstructured data. Responsibilities include collecting, cleaning, validating, and analyzing large datasets; building ETL pipelines; evaluating machine learning models; communicating findings to technical and non-technical stakeholders; mentoring users; and supporting mission-critical US PACOM analytics with an active Secret clearance.

Supports the analysis of complex data for National Reconnaissance Office mission efforts. Designs and integrates large datasets, evaluates data quality, prepares data for predictive modeling, and applies AI, machine learning, and statistical methods. The role also supports platform testing, quality assurance, process automation, technical documentation, data compliance, and ethical technology use while advising leadership on mission impact and data-informed decisions.

Build and operate AML data products, ETL pipelines, data models, integrations, and production data systems for financial crime prevention. Support machine-learning workflows, data quality, security, governance, observability, incident response, and operational resilience. Partner with AML experts on transaction monitoring, customer risk rating, sanctions screening, and surveillance initiatives. Improve engineering standards through reviews, mentoring, testing, secure coding, CI/CD, and automation.

Leverage healthcare platform data to identify behavioral patterns, develop product and client-facing analytics, and guide data strategy. Lead data projects from ingestion through analysis and recommendations while collaborating with product managers, analysts, and developers. Apply advanced statistics, SQL, Python, distributed processing, machine learning, and data visualization to communicate actionable insights to stakeholders.

Lead credit-risk analytics for consumers and small businesses using large payment/customer data sets. Build attributes and models, deliver executive-ready insights, monitor emerging risks, prioritize high-impact projects, and identify GenAI opportunities to modernize analytics.

Leads end-to-end data science projects for automated underwriting and related insurance analytics. Develops statistical and machine-learning models, evaluates performance, analyzes complex datasets, and implements scalable production solutions. Partners with underwriting, product, business, engineering, and technology teams; monitors models; documents governance and validation processes; evaluates external data and third-party solutions; provides technical direction; reviews work; and mentors other data scientists.

Lead product-focused data science and machine learning initiatives, including exploratory analysis, production model development, evaluation frameworks, generative AI, graph-based machine learning, recommendation systems, and personalization. Collaborate with engineering, product, and business teams to deliver high-impact solutions, influence strategy, and communicate insights to technical and non-technical stakeholders. The hybrid role requires four days per week in the Greater Boston area.

Design and develop advanced data science and AI models involving machine learning, optimization, simulation, generative AI, and agentic AI. Analyze massive datasets, engineer features, and deploy scalable production solutions in cloud environments with engineering teams. Translate complex analyses into strategic business insights, mentor data scientists, and influence business decisions. The role requires hybrid onsite work in Cincinnati, Ohio.

Develops production-grade statistical and machine learning models and predictive solutions for healthcare operations and business performance. The role analyzes complex data, designs experiments, evaluates AI systems, establishes metrics, creates dashboards, standardizes reporting, and communicates insights to technical and executive stakeholders. It also supports generative AI evaluation, model deployment workflows, and data-driven decision-making in regulated industries.

Leads delivery and sustainment of domain data pipelines and data products for defense programs. Owns roadmaps, prioritizes backlogs, designs solutions, ensures reliability, security, observability, data quality, and governance, and coordinates cross-domain integration. Provides technical leadership and mentoring to data engineering and operations teams while managing risks, dependencies, stakeholder expectations, and alignment with program objectives across constrained and classified environments.

Leads enterprise-scale data science and statistical modeling projects supporting government evaluations, ROI analysis, risk factors, and program effectiveness. Reviews analytical designs, code, and results; mentors data scientists; establishes reproducible methods and validation standards; develops dashboards, scorecards, KPIs, and performance measures; and advises government stakeholders on model interpretation and limitations.

The Principal Data Scientist will analyze large datasets, develop metrics systems, dashboards, business models, and A/B tests to improve MSN content relevance, recommendation performance, user engagement, and business growth. The role partners with cross-functional stakeholders, identifies trends and root causes, evaluates product performance, recommends improvements, and communicates actionable insights to technical and non-technical audiences.

Develops and deploys data science solutions for information operations and national security customers. Responsibilities include statistical modeling, machine learning, deep learning, simulation, network analysis, multilingual text analytics, data visualization, experimentation, prototyping, customer engagement, and technical translation. The role also supports deployment in cloud, containerized, and air-gapped environments, including configuration, monitoring, troubleshooting, and maintaining customer systems. An active Top Secret clearance with SCI eligibility is required, and the position is onsite in the DC Metro area up to four days weekly.

Build AI data and evaluation platforms for autonomous aircraft, transforming flight telemetry, sensor, imagery, and simulation data into curated datasets, metrics, and workflows. Develop scalable annotation, data quality, evaluation, visualization, and machine learning solutions for perception and decision-making. Collaborate with autonomy, controls, and flight software teams while supporting system reliability, anomaly investigation, safety validation, and production ML deployment.

Develops and validates predictive and prescriptive machine learning models for cyber defense, anomaly detection, event correlation, and threat characterization. Performs advanced statistical, operational, and unstructured-data analyses using high-performance computing environments. Translates mission requirements into analytic solutions, documents methodologies, collaborates with partners, and mentors junior data scientists.

Build and deploy production machine learning and AI solutions on Databricks and Azure. Responsibilities include data discovery, feature engineering, model development, experiment tracking, model serving, monitoring, retraining, LLM and RAG use cases, responsible AI documentation, and modernization of legacy R, Stata, and SAS workloads. The role also involves client consultation, cloud adoption, training, mentoring, and communicating results to technical and executive audiences.

Develop data science, machine learning, AI, statistical, and visualization solutions supporting government audit and investigative products. Build and optimize data pipelines, integrate diverse data sources, perform ETL and modeling, troubleshoot applications, and document technical designs. Collaborate with stakeholders, database administrators, and IT teams; provide technical guidance, training, and support. The role requires coding in SQL, Python, R, and JavaScript, cloud data platform experience, U.S. citizenship, and eligibility for Public Trust clearance.

Analyzes enterprise incident, problem, change, API performance, and latency trends using Splunk, cloud observability tools, statistical modeling, AI, and machine learning. Develops predictive models, prototypes, dashboards, and remediation strategies to identify anomalies, improve automation, and resolve systemic issues across enterprise platforms. Collaborates with application, infrastructure, and network teams while providing technical guidance to stakeholders.

Lead performance marketing data science initiatives focused on lead segmentation, bidding optimization, targeting, and marketing spend allocation. Develop and deploy production-ready machine learning systems, establish research and ML operations roadmaps, build monitoring frameworks, and identify high-impact opportunities. Partner with engineering, marketing, vendors, and executives while mentoring data scientists and machine learning engineers and promoting rigorous, maintainable, and reproducible practices.

Lead data science for Mozilla’s advertising business by identifying high-impact opportunities, owning complex analyses, developing measurement and experimentation frameworks, analyzing revenue and performance, and translating findings into recommendations for technical partners and executives. Partner across Product, Engineering, Sales, Finance, and leadership to support business decisions in a fast-moving environment while balancing analytical rigor, pragmatism, and user privacy.

Lead data science partnership for Mozilla’s advertising business, identifying high-impact opportunities and owning complex analytics end to end. Develop measurement frameworks, experimentation strategies, instrumentation, and reporting for advertising products. Analyze revenue and performance, evaluate trade-offs, and communicate clear recommendations and uncertainty to technical teams and senior executives.

Provides SETA support for NRO Ground Enterprise acquisition and lifecycle management. Analyzes data to improve AI/ML transparency and explainability, develops and evaluates machine learning models, supports software architecture and cloud-based systems, identifies technical debt and risks, creates technical roadmaps and acquisition documentation, and advises government stakeholders on automated analytics and computer vision capabilities. Requires SharePoint development, web technologies, automation tools, DevSecOps exposure, agile and cloud knowledge, and active TS/SCI clearance with polygraph.

Owns product measurement and decision thresholds across Ascend’s financial operations platform. Responsibilities include defining accuracy, success, and exception metrics; auditing production scores; partnering with Product and Engineering on instrumentation; deploying models for confidence scoring, reconciliation, collections, and recovery; monitoring calibration, drift, and realized outcomes; designing experiments; and using product data to inform the roadmap.

Develops predictive models, quantitative analyses, data visualizations, and machine learning solutions for Department of Defense clients. Responsibilities include data exploration, cleaning, mining, statistical programming, natural language processing, text mining, dashboard development, and advising stakeholders on actionable insights. Requires experience with Maven Smart System and a TS/SCI clearance.

Develop machine learning, statistical, data mining, graph-based, and heuristic algorithms for national security applications. Build and optimize AI/ML and large language models, including information retrieval workflows. Analyze varied datasets, create feature vectors, design experiments, evaluate models using standard metrics, produce visualizations, and translate subject matter expertise into production analytics. Guide analytic development efforts and integrate prototype algorithms into analyst workflows.