Top Data Science Jobs

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.

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.

Own value-based bidding across digital marketing channels by building customer lifetime value models, integrating predictions with Google Ads and Meta, designing incrementality experiments, and advising Marketing on budget allocation, audience strategy, campaign measurement, and funnel performance. The role requires production-oriented machine learning, statistical analysis, automated data integrations, and collaboration with Pricing and Profitability teams.

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 ecosystem-wide measurement strategy for Microsoft AI products. Define metrics, experimentation frameworks, causal-inference analyses, and evaluation systems that guide product strategy, investment, and resource allocation. Partner with product, engineering, business, and executive leaders to identify opportunities, quantify impact, and align organizations. Provide technical leadership, mentorship, and analytical best practices across large-scale data science and experimentation initiatives.

Leads development of machine learning, optimization, GenAI, and stochastic simulation solutions for clinical trial operations. Builds forecasting models, optimization engines, LLM applications, and explainable decision-support systems using clinical, operational, real-world, and financial data. Supports enrollment forecasting, cost estimation, site selection, protocol analysis, and operational planning. Communicates technical findings to diverse stakeholders and coaches junior colleagues.

Develop and deploy enterprise machine learning and data science solutions across the full lifecycle. Responsibilities include statistical analysis, predictive modeling, feature engineering, data exploration, model deployment and monitoring, MLOps, ETL, and business intelligence reporting. The role collaborates with engineers, product managers, stakeholders, and cross-functional teams to deliver scalable solutions using cloud platforms, large-scale data, and modern machine learning technologies.

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.

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.

Lead end-to-end data science for lending products: define measurement frameworks, analyze experiments and cohorts, evaluate risk and model performance, use AI tools to accelerate analysis, build reusable tooling, and mentor junior scientists while partnering with product, engineering, and risk stakeholders.

Lead end-to-end data science for lending products: define measurement frameworks, analyze customer and risk data, design experiments, monitor model and policy performance, and build reusable tooling. Use AI tools and agent workflows to accelerate analysis, mentor junior scientists, set technical standards, and partner with product, engineering, and risk to drive product and business decisions.

Lead end-to-end data science for lending products: define metrics, run experiments, build measurement systems, evaluate model and policy performance, use AI tools to accelerate analysis, and mentor the team while partnering with product, engineering, and risk stakeholders to drive credit product decisions.

Own and deliver production machine learning workstreams for utility bill extraction, forecasting, and audit/anomaly detection. Build and evaluate models, establish technical approaches for ambiguous problems, write production-grade Python, and partner with engineering on integration and monitoring. The role also involves applying LLMs and agentic workflows, automating team processes, prioritizing investments, and communicating model behavior and recommendations to technical and nontechnical stakeholders.

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.

Develop and deploy advanced AI, machine learning, deep learning, and NLP models for complex legal-data problems. Build legal reasoning, question-answering, drafting, predictive, recommendation, and document-classification systems. Design production APIs, scale data and ML infrastructure, collaborate cross-functionally, and mentor junior data scientists. The role requires expertise with large datasets, LLMs, distributed computing, cloud platforms, and MLOps.

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.