Top Data Science Jobs

Own revenue-focused data science from opportunity sizing through production ML. Investigate and measure product changes, build and operate batch and online ML systems for personalization, experimentation, and automation, and partner cross-functionally to integrate models into user-facing products. Establish monitoring, lineage, reproducibility, and security practices; improve data pipelines; mentor data scientists; and define ML development best practices while using AI tools to accelerate delivery.

Design, develop, deploy, and monitor advanced deep learning models for fraud detection, risk management, and identity verification. Lead machine learning initiatives, research novel algorithms, manage end-to-end model lifecycles, collaborate with Product, Engineering, and Risk teams, and communicate insights to technical and executive stakeholders. Mentor data scientists and drive high-impact delivery, data quality, experimentation, and continuous improvement.

Design and deploy optimization and machine learning models for allocating scarce parts and resources across competing programs. Responsibilities include formulating MILP and constraint optimization models, forecasting demand and supply risk, building data pipelines, validating outputs, communicating tradeoffs to stakeholders, mentoring junior data scientists, and partnering with software engineers to productionize scalable services.

Performs quantitative analysis, data integration, exploratory analysis, visualization, and machine learning to uncover insights from complex datasets supporting intelligence community missions. Uses Python and SQL to process data, develop predictive models, and communicate findings to technical and nontechnical stakeholders. Requires a TS/SCI clearance and bachelor's degree, with preferred experience in Spark, JEMA, web development, cloud services, ETL, geospatial data, and dashboards.

Leads the data science platform and machine learning strategy, building data marts, predictive models, MLOps infrastructure, and analytics capabilities. Analyzes healthcare claims, clinical, workflow, and operational data to risk-stratify patients, assess clinical program impact, support dashboards, and improve patient and provider engagement. Collaborates with clinical, operations, finance, product, executive, and technology teams while serving as a trusted analytics expert for a business workstream.

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.

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.

Embeds with strategic customers to design, build, and implement AI-driven solutions that scale support operations and drive measurable Fin adoption. Analyzes customer workflows and data, establishes measurement best practices, prototypes production-aware tools, and partners with Sales, Success, Product, and Engineering. The role also feeds deployment insights into product strategy and requires strong communication, adaptability, statistical analysis, SQL, Python, and experience with AI/ML evaluation or LLM applications.

Architects and operates enterprise AI/ML data platforms supporting vector search, RAG, LLM applications, and Agentic AI. Establishes data governance, lineage, traceability, explainability, monitoring, and evaluation controls. Partners with engineering, cybersecurity, architecture, data science, and mission stakeholders to deliver secure, scalable cloud-native capabilities. Evaluates emerging AI technologies, coordinates enterprise initiatives, and communicates technical risks, tradeoffs, and mission impacts to senior leadership.

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.

Lead advanced data science for Unity’s advertising business, analyzing revenue, supply, demand, and customer behavior. Define metrics, evaluate ML and product initiatives, guide roadmaps, and turn large-scale data into executive-level recommendations. Partner with Product, Engineering, Sales, and Finance, while mentoring data scientists and providing technical leadership without formal management authority.

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.

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.

Develop and deploy machine learning models for personalization, customer journey optimization, recommendation systems, behavioral segmentation, and digital marketing effectiveness. Partner with engineering, marketing, sales, UX, and IT teams to build scalable data products, conduct A/B tests, translate business needs into data science solutions, and communicate actionable insights. Apply statistical analysis, causal inference, and emerging AI techniques while following documentation and model version-control best 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.

Leads the design, evaluation, deployment, and monitoring of production AI systems across generative AI, agentic AI, computer vision, forecasting, and optimization. Establishes rigorous metrics for accuracy, uncertainty, bias, hallucinations, drift, explainability, and failure modes; conducts experiments and communicates business impact, risks, and trade-offs. Builds solutions using foundation models, RAG, agentic workflows, unstructured data, and MLOps practices.