Top Data Science Jobs

The Data Scientist develops predictive models, segmentation frameworks, machine learning solutions, data pipelines, and experiments supporting marketing and customer analytics. The role analyzes complex datasets, validates and monitors models, communicates insights to technical and executive audiences, and partners with strategy, media, client services, and engineering teams to deliver scalable data-driven solutions.

Designs and deploys predictive models, segmentation frameworks, machine learning solutions, and data pipelines for marketing and customer analytics. Analyzes complex datasets, conducts experiments, validates and monitors models, and translates findings into actionable recommendations. Collaborates with strategy, media, client services, and engineering teams while communicating insights to technical and executive audiences. The role also supports peer development, documentation, knowledge sharing, and adoption of emerging data science practices.

Designs and deploys predictive models, segmentation frameworks, machine learning solutions, and data pipelines supporting marketing performance and customer analytics. Analyzes complex datasets, conducts experiments, validates and monitors models, and communicates insights to technical and non-technical stakeholders. Partners with strategy, media, client services, and engineering teams to create scalable data-driven solutions while contributing to peer review, knowledge sharing, and evolving data science practices.

Designs and deploys predictive models, segmentation frameworks, machine learning solutions, data pipelines, and experiments supporting marketing and customer analytics. Analyzes complex datasets, validates and monitors models, communicates insights to technical and executive audiences, and partners with strategy, media, client services, and engineering teams to deliver scalable data-driven solutions.

Analyze complex aerospace and defense datasets to develop predictive analytics, optimization algorithms, machine learning solutions, scripts, dashboards, and automated processes. Collaborate with data engineers, modelers, web developers, and customers to understand problems, interpret data, and deliver actionable insights supporting military leadership and mission solutions.

Lead and build Neural Earth's geospatial data science function: design and deploy Python-based geospatial models, architect production geospatial data pipelines, validate reproducible methods, translate scientific outputs into customer-ready products, and manage and mentor at least one direct report while advising customers and stakeholders.

Develop algorithms, predictive models, quantitative analyses, and visualizations from structured and unstructured data. Apply statistical programming, data mining, machine learning, AI, and NLP techniques to solve client challenges across public and private sectors. Collaborate with clients to understand analytical needs, explore and clean data, and translate complex datasets into actionable insights. The role requires the ability to obtain a Secret clearance and a bachelor's degree.

Develop machine learning and data science solutions for intelligence operations and special operations missions. Build geospatial and 3D computer models, select and tune machine learning algorithms, create predictive models, conduct quantitative analyses, and visualize data. Collaborate with clients and guide algorithm and system development using disparate data sources and modern data and computing tools.

Develops NLP and machine learning solutions for VA disability claims adjudication. Responsibilities include extracting medical and claims data, building NLP pipelines, tuning transformer models, testing accuracy, documenting workflows, collaborating with VA stakeholders, and integrating models into VA systems. The role requires Python, healthcare and claims data experience, cloud NLP tools, SQL, and familiarity with clinical terminology and coding.

Provides technical leadership for document verification machine learning solutions. Oversees the full ML lifecycle, including data strategy, model development, evaluation, deployment, monitoring, and improvement. Mentors data scientists, establishes technical standards, identifies risks, and improves model performance, scalability, and reliability. Partners with engineering, product, and customers to translate needs into modeling strategies and communicates technical decisions to diverse stakeholders.

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.

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.

Applies statistical analysis, data mining, machine learning, forecasting, NLP, and visual analytics to human performance, behavioral health, and readiness data. The role prepares and cleans data, develops predictive and prescriptive models, builds Power BI dashboards, automates recurring reports, documents reproducible methods, and communicates insights to government, clinical, performance, and command stakeholders. It requires strong Python or R, SQL, statistics, machine learning, data preparation, and stakeholder communication skills, with preferred experience in Databricks, Spark, NLP, Power Platform, and health-related data.

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.

Develops and evaluates statistical, machine learning, and AI models for internal business initiatives. Responsibilities include data cleaning, feature preparation, exploratory analysis, prompt engineering, LLM applications, hypothesis testing, time series analysis, validation, deployment support, documentation, and translating results into actionable insights. Collaborates with senior data scientists, analytics engineers, and business stakeholders. This is an in-office role requiring residence in the metro Phoenix area.

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.

Data Scientist partnering with cross-functional teams to define metrics, design experiments, uncover insights, build analytical models, and develop data tools and systems. The role owns complex data projects end-to-end, supports product, marketing, finance, and platform initiatives, and promotes data quality and accessibility. Candidates need strong SQL, scripting, distributed data systems, statistical methods, experimentation, forecasting, and stakeholder communication skills.

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.

Develops and evaluates machine learning models for United States Air Force mission problems. Responsibilities include data preparation, feature engineering, statistical analysis, model training and validation, performance monitoring, data governance, DevSecOps, compliance documentation, and dashboard development. Collaborates with government stakeholders, engineers, cybersecurity personnel, architects, and mission users while maintaining auditable model lifecycle records and adhering to TS/SCI security requirements.

Analyze large-scale user and app datasets to generate actionable insights for advertising campaign performance and product strategy. Partner with business and technical teams on rigorous analysis, testing, and opportunity identification. Communate findings and recommendations to stakeholders, while using market and performance data to influence product and machine learning roadmaps. The role requires strong statistical reasoning, hypothesis testing, Python, SQL, and data analysis capabilities.

Develops and deploys AI and LLM-driven features for Microsoft 365 Copilot, including model evaluation, metrics, experimentation, retrieval-augmented generation, prompting, and agentic system optimization. The role prototypes scalable AI applications using Azure AI Foundry and LangChain, diagnoses model issues, and supports improvements to deep learning, NLP, and multimodal systems.