Top Data Science Jobs

Own audience or measurement products for Fetch’s monetization business, translating advertiser goals and purchase behavior into predictive models, datasets, and causal studies. Build and validate audience expansion, measure advertising outcomes, assess bias and uncertainty, and improve products using campaign results. Partner with Product, Engineering, Sales, and GTM, present findings to customers, and develop reproducible analyses and production-ready assets.

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.

Build, evaluate, deploy, and monitor identity-resolution and record-matching models using large, noisy datasets. Design labeling and measurement strategies where no definitive answer key exists, use AI-assisted labeling, manage model retraining and lineage, review data quality, and communicate results and tradeoffs to product, engineering, and sales stakeholders. Mentor teammates and ensure responsible handling of personal data, opt-outs, retention, and lineage requirements.

Lead data science initiatives involving entity and identity resolution, knowledge graphs, information extraction, search, predictive modeling, and AI-enabled discovery. Design data architectures and machine learning workflows, establish model development and monitoring standards, guide scalable data products, evaluate emerging technologies, and brief senior leadership on AI strategy, technical risks, and investment opportunities.

Develop and deploy machine learning and predictive models for banking and financial services clients. Responsibilities include feature engineering, model training and validation, credit and risk modeling, fraud detection, production-ready Python development, stakeholder engagement, MLOps collaboration, model monitoring, and communicating technical findings and business impact.

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.

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.

The Senior Data Scientist will develop, validate, and operationalize AI models for diagnosing complex diseases, enhance data analysis processes, and establish MLOps infrastructure while leading data-driven initiatives.

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.

Lead end-to-end deep learning development in a Databricks Lakehouse environment, including data pipelines, feature engineering, distributed training, model evaluation, inference, monitoring, and governed deployment. Build scalable batch and real-time ML systems using Spark, MLflow, and Databricks. Fine-tune large and foundation models, optimize performance and cost, and collaborate cross-functionally to productionize machine learning solutions.

Own end-to-end data science and analytics projects using Python, machine learning, and time-series analysis to improve drilling safety and efficiency. Develop emissions, advisory, monitoring, alerting, and equipment-failure prediction models. Mentor data scientists and analysts, communicate findings to technical and nontechnical stakeholders, maintain code standards, support analytics applications, and occasionally visit drilling rigs to understand operations and test technology.

Develops reliability, statistical, and data science models for solid rocket motor design, production, testing, and sustainment. Responsibilities include failure analysis, uncertainty quantification, manufacturing variability, predictive cost modeling, performance-test analysis, material-property databases, aging and service-life estimates, and technology roadmaps. The role collaborates extensively with engineering, quality, operations, supply chain, and program management teams.

Lead end-to-end data science and machine learning projects, from scoping ambiguous client problems through production deployment. Build and evaluate statistical and ML models, analyze large datasets, run experiments, collaborate with engineers, create reusable tooling, communicate findings to diverse stakeholders, and mentor data scientists. The role combines hands-on data science with technical consulting and requires occasional customer on-call support.

Lead data science strategy for newsroom audience analytics by building predictive and explanatory models, metrics, forecasts, and measurement systems. Partner with editorial, audience, product, and digital strategy teams to guide content and engagement decisions. Apply causal inference, experimentation, and statistical methods; translate findings into recommendations; maintain production analytics workflows; and mentor junior data scientists while advancing technical standards.