Top Data Science Jobs

Leads enterprise data science, AI, machine learning, and advanced analytics strategy supporting NASIC. Designs analytical architectures, optimizes complex data workflows, develops governance frameworks, evaluates cloud capabilities, and guides AI/ML deployment. Produces executive dashboards and progress reports, creates technical policies, and coordinates with data, AI, infrastructure, and intelligence stakeholders. The role requires extensive computational experience, DoD/IC standards expertise, cloud proficiency, and an active TS/SCI clearance.

Develops and deploys AI/ML and intelligent automation workflows for complex intelligence datasets. Builds statistical models, data pipelines, dashboards, and scalable software outputs within secure cloud infrastructure. Integrates data strategies with Zero Trust Architecture, supports reporting and technology prototyping, and collaborates with engineers, database administrators, and product owners. Requires active TS/SCI clearance and onsite support at Wright-Patterson AFB.

Develops automated collection models, analytic models, workflow automations, and data-processing solutions supporting intelligence strategies. Evaluates and improves automated solutions using performance data and stakeholder feedback. Applies data science, visual programming, geospatial tools, statistical methods, machine learning, and visualization to address intelligence integration, automated collection, information sharing, and analytic workflow challenges.

Develop and maintain operational data pipelines supporting multiple data formats and interfaces. Query systems and migrate data using APIs, work with SQL or NoSQL databases and cloud architectures, and use Python, JavaScript, or Java. Monitor data pipelines, communicate complex technical concepts, and potentially support NGA enterprise solutions, MLOps, ATO processes, SAFe implementation, and dynamic visualizations.

Builds and maintains data structures, ETL scripts, dashboards, data visualizations, and automation routines. Performs data cleaning, validation, metadata governance, and basic AI/ML algorithm execution. Supports Agile sprint activities, pipeline documentation, database maintenance, and troubleshooting of ingestion and dashboard issues. Requires onsite work at Wright-Patterson AFB supporting NASIC.

Supports the design, testing, calibration, and deployment of predictive analytics models for Naval mishap risks and safety hazards. Performs feature engineering, statistical modeling, forecasting, causal inference, anomaly detection, cross-validation, and model evaluation. Collaborates with data scientists and software engineers to transition models into operational environments, participates in technical exchanges, and assists with model training and interpretation.

Build and deploy data science models for CPG brands, focusing on demand and sales forecasting, promotion effectiveness, pricing, elasticity, and cannibalization. Analyze messy retail, inventory, trade promotion, and supply-chain data; develop statistical validation and evaluation methods; and communicate model insights and uncertainty to technical and non-technical stakeholders. Collaborate with AI/ML engineers to move analyses into production.

Drive product analytics across feature adoption, usage, pricing, customer health, and experimentation. Partner with product, engineering, and business leaders to identify opportunities, deliver actionable insights, improve self-serve analytics and governance, and strengthen data pipelines and analysis quality. The role requires communicating complex findings clearly and working from Aircall’s San Francisco office two days per week.

Designs and maintains large-scale APIs and systems supporting money movement, translates product requirements into technical specifications, and guides product strategy. The role requires data science or quantitative modeling expertise, production model development, experimentation, SQL, Python or R, and translating analysis into business outcomes. Responsibilities also include reviewing API launches, ensuring system compatibility and extensibility, representing the team in technical discussions, mentoring engineers, and establishing engineering principles.

Develop NLP, LLM, and machine learning pipelines to abstract clinical concepts from structured, unstructured, and multimodal healthcare data. Query healthcare source systems, curate and validate AI-ready datasets, support patient phenotyping and real-world evidence analyses, and deliver HIPAA-compliant data products. Collaborate with product and engineering teams, communicate findings to technical and clinical audiences, and improve data quality for customer AI applications. The role is remote with occasional quarterly travel.

Partner with clients to design healthcare data science solutions, curate multimodal clinical datasets, and conduct statistical, predictive, causal inference, real-world evidence, and AI/ML analyses. Responsibilities include querying EMR and other health data sources, creating statistical analysis plans, managing SQL and analytical programming, ensuring HIPAA-compliant data quality, documenting findings, and presenting results to technical, clinical, and executive audiences.

Lead data science at Bonfire by solving complex analytical problems, building predictive models and scalable Databricks data pipelines, and shaping AI-powered products. Own customer projects from discovery through delivery, translate findings into clear reports and presentations, establish technical standards, identify new analytical products, and mentor future team members. The role involves working with large healthcare claims datasets and collaborating closely with customers and founders.

Analyze large-scale financial datasets, develop statistical and machine learning models, extract business insights, and communicate technical findings to non-technical stakeholders. The intern will collaborate with data scientists, investment bankers, portfolio managers, and senior management while contributing to AI and data solutions across Lazard.

Own the analytics roadmap for a Consumer Banking customer pillar, leading measurement, experimentation, and analyses across growth, retention, pricing, servicing, and strategy. Translate complex business questions into executive-ready recommendations, influence cross-functional decisions, establish analytical standards, and mentor analytics talent. The role requires strong statistical, experimental, communication, visualization, and stakeholder-management skills.

As a Data Scientist, you will perform ETL/data cleaning, develop visualizations, build predictive models, and support AI solution implementation.

Analyzes complex datasets using statistical methods, machine learning, and AI techniques. Builds and evaluates predictive models, develops reproducible analytical workflows, supports production deployment, and applies NLP, generative AI, LLMs, deep learning, and time-series methods. Partners with engineers and stakeholders to deliver mission-focused solutions, documents findings, and supports responsible AI practices including explainability, trustworthiness, and risk management.

Optimize dataset quality and value through marketplace incentives, task targeting, bonuses, payouts, eligibility, and experimentation. Design treatment assignments and holdouts, evaluate incremental collection against objective functions, identify Goodhart effects, and translate findings into operational actions. Partner with software teams to ship incentive and payout changes into the collection product. Experience with marketplace experimentation, pricing, growth, real-money payouts, or product implementation is preferred.

Develop statistical models, forecasting methods, and machine learning algorithms for airline optimization and travel retailing. Source and manipulate data using multiple programming languages, deploy models in cloud environments, and collaborate with software engineers on production analytics tools. Define KPIs, monitor data and model performance, conduct proof-of-concept studies, and communicate insights to clients and internal stakeholders.

Develop statistical methodologies and data science solutions for consumer purchase panel data. Build Python and SQL workflows, metrics, monitoring, and validation frameworks; investigate data quality and consumer behavior; and support projects from exploration through deployment. Collaborate with Product, Engineering, Data Operations, and business partners while communicating methodologies, findings, assumptions, and tradeoffs to technical and non-technical audiences.

Leads and develops AI, machine learning, and advanced analytics solutions that deliver customer value. Translates requirements into deployable solutions, identifies and prioritizes projects, collaborates with functional and IT leaders, mentors junior team members, and supports model validation, reproducibility, deployment, and continuous improvement initiatives.

Develop and administer Splunk environments, including distributed architecture, data pipelines, client and app management, integrations, searches, alerts, reports, dashboards, and configuration files. Automate tasks using Bash, Python, and PowerShell; develop custom apps and regular expressions; manage Linux systems; and interface programmatically with AWS using its SDK and CLI. An active TS/SCI clearance with full-scope polygraph is required.

Develops and evaluates algorithms for synthetic aperture radar imagery, conducts quality trade studies, prototypes analytical tools, generates confidence metrics, and refines verification and validation processes. Builds software and scripts in Python using Databricks and Jupyter Notebook, supports CI/CD workflows with GitHub, implements and trains algorithms, and delivers technical presentations and reports to government customers.

The Cyber Data Science Engineer analyzes large cybersecurity datasets, develops data-driven solutions, evaluates vulnerability and compliance data, creates visualizations and reports, modernizes Excel-based situational awareness tools, and supports risk management automation and visualization.

The Director of Data Science role at Capital One involves leading a team to build data pipelines and machine learning models, leveraging technologies like Python and AWS to derive insights for improving customer financial decisions.

Lead development of statistical and machine learning models for time-series, geospatial, kinematic, and radar data. Build analytic solutions, training datasets, ETL workflows, forecasting and anomaly-detection models, simulations, visualizations, and performance metrics. Integrate multi-source sensor data, enhance algorithms, document processes, and collaborate with engineers, analysts, mission partners, and stakeholders in an agile environment.