Top Data Science Jobs

Leads enterprise-scale data science and statistical modeling projects supporting government evaluations, ROI analysis, risk assessment, and intervention effectiveness. Develops, validates, automates, and delivers predictive models, dashboards, scorecards, and KPIs. Reviews analytical designs and code, establishes reproducible standards, advises government stakeholders, and mentors data scientists. Requires advanced Python or R and SQL skills, quantitative education, statistical modeling experience, and Top Secret clearance or eligibility for Top Secret/SCI.

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.

Develops data quality checks, metadata standards, and AI use-case inventories for HHS. Cleans and integrates complex datasets, applies machine learning and NLP for automated tagging and classification, evaluates AI governance risks, and produces reporting datasets, dashboards, and recommendations. The role requires translating ambiguous federal policy and reporting requirements into repeatable analytical workflows while collaborating with product, design, engineering, and government stakeholders.

Develop and deploy machine learning models and scalable data pipelines for pharmaceutical sciences and translational safety. Build AI-ready datasets from toxicology, PK/PD, in vivo, biomarker, pathology, and assay data. Collaborate with toxicologists, pharmacologists, safety professionals, and data engineers to support safety signal detection, dose selection, translational research, and scientific decision-making. Apply model governance, data standardization, ontology practices, validation, and cloud-based engineering techniques.

Develops machine learning models and production data science pipelines to improve subscription conversion, content recommendations, user understanding, workflow automation, and product decisions. Leads analytical projects and experiments, applies statistical methods to web-scale data, and collaborates with data, product, growth, and engineering teams. The role also communicates complex findings to diverse audiences and helps foster a strong data science culture.

Develop and deploy machine learning and LLM-based solutions for cybersecurity challenges. Responsibilities include analyzing complex datasets, building classification and NLP models, optimizing prompting, embeddings, RAG, and fine-tuning techniques, designing evaluation methodologies, and integrating models into scalable production systems. The role requires collaboration with cybersecurity researchers, software engineers, data engineers, and product managers.

Design, deploy, and maintain scalable machine learning solutions in production. Build end-to-end ML pipelines, predictive models, forecasting systems, and feature engineering workflows. Analyze large datasets, generate business insights, collaborate with engineering teams, monitor model performance, and deliver production-ready solutions using Python, Snowflake, MLOps, Scikit-learn, Pandas, NumPy, and SQL.

Leads development and adoption of operations research and decision intelligence solutions across pharmaceutical therapeutics development and supply. Builds forecasting, optimization, simulation, digital twin, and decision-support models for portfolio, resource, laboratory, process, and clinical supply planning. Provides technical leadership through reusable frameworks, software and model validation practices, stakeholder communication, mentorship, and cross-functional collaboration with scientists, engineers, supply chain, technology, finance, and senior leaders.

Develop and evaluate AI, generative AI, LLM, RAG, and agentic workflow solutions for pharmaceutical development, manufacturing, regulatory, quality, and supply-chain processes. Build reusable components, apply responsible AI and software engineering practices, validate outputs, and collaborate with scientific and technical stakeholders in regulated environments. The role requires Python development, pharmaceutical CMC knowledge, and communication with technical and non-technical teams.

Leads the design, development, validation, and deployment of machine learning and AI solutions for global Healthcare Compliance. Builds scalable data pipelines, risk-monitoring models, NLP and generative AI applications, Copilot agents, and analytical workflows. Partners with compliance, governance, technology, and business stakeholders to operationalize responsible AI, translate requirements into solutions, lead proofs of concept, and mentor team members.

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.

Develop and validate applied AI, machine learning, and statistical models supporting pharmaceutical development, manufacturing, clinical supply, quality, regulatory, and CMC activities. Responsibilities include exploratory data analysis, feature engineering, model tuning, software development, testing, documentation, reproducibility, and cross-functional collaboration with scientific and technical teams.

Measures customer engagement impact on pipeline, renewals, account health, and predictive model performance. Conducts baseline, holdout, before-and-after, and control-group analyses; validates customer data and helps tune models. Builds reusable value analyses, dashboards, and reporting for leadership, Customer Success, and Data Science. Requires strong SQL, Python, statistics, experiment design, business communication, and experience translating messy data into trusted insights.

Develop and standardize analytical methods for inferring residence, displacement, and poverty from mobile network operator data. Assess data quality, write technical specifications and tests, guide operator engineering teams, tune statistical methods, and balance accuracy with implementation constraints. Design privacy-preserving analyses, improve data security practices, train internal teams, and support international workshops and emergency-response programs.

Lead data science and AI consulting engagements for technology, media, and entertainment clients. Develop Agentic AI solutions, statistical models, experiments, KPIs, and advanced analytics to solve business problems. Manage client relationships, teams, staffing, quality assurance, stakeholder communication, and executive presentations. Support proposals, business development, thought leadership, training, and growth of the analytics practice.

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.

The Senior Data Scientist will conduct product analysis, build advanced models and analytics tools, evaluate A/B tests, develop automated dashboards, and generate recommendations for product strategy. The role partners with business stakeholders and Engineering to improve data infrastructure and governance, communicate statistical insights, and support data-driven decisions across Product, Marketing, and Finance.

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.

Develop and own advanced machine learning and AI models using bank transaction and cashflow data to enable financial product capabilities, manage risk, and drive profitable growth. Design experiments, build proofs of concept, monitor production models, and collaborate with data and machine learning engineering teams on scalability. Communicate findings and tradeoffs to business stakeholders while applying statistical, research, and financial services expertise.

Build predictive and machine learning models for patient needs, operational forecasting, churn, advocate quality, and risk. Design A/B tests and causal inference studies, apply advanced statistical methods, productionize models with data engineers, and communicate insights to business stakeholders. The role requires Python, advanced SQL, forecasting expertise, statistical proficiency, data privacy awareness, and the ability to work independently in a fast-paced healthcare startup.

Provides advanced data science and machine learning expertise using complex member, financial, and organizational data. Develops predictive and prescriptive models, algorithms, automated processes, and actionable insights for strategic decision-making. Leads projects, supports ethical AI practices, communicates findings to stakeholders, serves as a technical resource, and mentors junior staff. Applies statistical methods, advanced analytics, data visualization, and potentially NLP and large language models to solve complex business problems.

Technical lead for Quantitative Intelligence, setting strategy across predictive modeling, panel weighting, campaign measurement, and production ML. Responsibilities include developing forecasting and propensity models, causal measurement methodologies, privacy-preserving techniques, scalable data pipelines, evaluation frameworks, and governed model outputs. The role mentors senior scientists and engineers, communicates methodology to clients and stakeholders, and may progress into formal people management.

Own and advance production predictive intelligence systems, beginning with 30-day consumer spend prediction. Lead model development, experimentation, evaluation, feature pipelines, forecasting, privacy-preserving ML, production scoring, and quality monitoring at scale. Diagnose model and data issues, optimize accuracy against compute costs, publish governed model outputs, mentor junior data scientists, direct AI coding agents, and communicate results to technical and non-technical stakeholders.

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.