Top Data Science Jobs

Design and build thermal performance analytics, Python-based engineering models, scalable data flows, and Edge-native applications for power plant asset performance management. Integrate historian, cloud, Edge, and industrial systems; develop automation, diagnostics, KPI validation, and root-cause workflows; support customer deployments; and lead architecture discussions while mentoring engineering teams.

Leads the design and evolution of scalable statistical computing platforms, automation workflows, reusable programming tools, and data visualization capabilities for clinical development. Provides technical strategy, architecture guidance, regulatory-aware solutions, and leadership across global, cross-functional initiatives. Advises senior stakeholders, drives standards and tool adoption, resolves complex scientific computing challenges, and mentors teams. The role supports reproducible, compliant, high-performance evidence generation using Python, R, and advanced data science practices.

Develop algorithms, predictive models, quantitative analyses, and visualizations using structured and unstructured data. Apply machine learning, artificial intelligence, natural language processing, statistical programming, and data mining to help clients solve complex problems across public and private sectors. Collaborate with clients to understand needs, uncover insights, and transform disparate data into actionable information. The role requires a Secret clearance and a bachelor’s degree.

Develop predictive models, algorithms, quantitative analyses, and data visualizations from structured and unstructured data. Explore, clean, analyze, and interpret complex datasets using statistical programming, machine learning, NLP, and text-mining techniques. Collaborate with clients to identify data-driven solutions, lead solution development for complex programs, guide teammates, and provide insights supporting informed decisions.

Develop algorithms, predictive models, and data-driven systems using structured and unstructured data. Perform data exploration, cleaning, analysis, visualization, mining, and modeling to answer client questions and support informed decisions. Apply statistical programming, machine learning, artificial intelligence, and natural language processing techniques across public- and private-sector challenges. Collaborate with clients in data-rich environments and translate complex datasets into objective, actionable insights.

Lead scalable simulation-data systems for robotic motion AI. Design synthetic data pipelines, simulation environments, domain-randomization strategies, curricula, and recovery-data generation. Build systems to replay and perturb robot trajectories, while establishing dataset provenance, metadata, versioning, and reproducibility. Collaborate with model, testing, regulatory, product, and clinical teams to train and validate algorithms for safety-critical robotic systems.

Builds real-time generative AI and virtual-agent tools for voice and messaging support channels. Responsibilities include developing and deploying scalable machine learning solutions, experimenting with LLMs, designing prompts, analyzing interaction metrics, creating data pipelines and Flask applications, identifying AI opportunities, defining KPIs, and communicating results to executives. Requires collaboration across product, engineering, analytics, and operations teams.

Supports data processing, statistical and machine learning model updates, marketing analyses, reporting, and data automation. Uses R or Python, SQL, BigQuery, BI tools, and spreadsheets to prepare datasets, maintain media-mix models, analyze results, and communicate insights to technical and non-technical audiences. Collaborates with Engineering, Reporting, Product, Paid Media, and Data Science teams while developing advanced analytical skills.

Leads the end-to-end design, deployment, and governance of AI-driven pricing models across merchandise categories. Builds data acquisition and curation pipelines, applies machine learning and Gen AI to product matching and data cleanup, integrates competitive pricing data, and develops experimentation infrastructure. Partners with product, engineering, analysts, and executives to optimize pricing performance, investigate anomalies, guide AI adoption, and establish modeling standards while mentoring data science and pricing teams.

Build predictive and cohort models supporting underwriting and marketing. Source, evaluate, combine, and analyze internal and external data; develop analytical datasets and solutions; implement and monitor models in production; communicate insights to business partners; document methodologies and results; and support statistical analysis requests. May supervise 1–2 analysts and provide guidance on cohort modeling and related analytics techniques.

Administer data management and analytics platforms, manage ingestion and data quality, analyze complex structured and unstructured datasets, and develop reports, dashboards, and analytical products. The role supports AI/ML workflows including NLP, semantic search, and information retrieval, while partnering with engineers and government customers. Responsibilities also include translating mission requirements into data solutions, troubleshooting systems, protecting classified data, and supporting demonstrations and deployments in customer environments.

Support finance and operations analyses by querying Ivy's Snowflake data lake, joining and cleaning large datasets, building data models, producing visualized reports, and delivering actionable insights. Collaborate with stakeholders on ad hoc and strategic projects, and apply statistical and machine learning fundamentals to operational problems.

Lead advanced marketing analytics engagements, including media mix modeling, attribution, forecasting, experimentation, and optimization. Develop marketing science frameworks, bespoke analytical offerings, proprietary marketing technology, and new modeling datasets. Communicate statistical findings to clients and senior stakeholders, influence implementation, and support new business development and client relationships.

Design and deploy predictive models and reproducible pipelines for voter and survey data. Build data infrastructure, automate workflows, develop tools and internal platforms, apply ML and AI for survey analysis, and communicate insights to non-technical stakeholders to inform political and policy strategy.

Support DIA EWOKS by cleaning and preprocessing data, performing routine quality checks and basic analysis, developing dashboards (Tableau/Power BI), creating complex visualizations, and applying AI/ML models for unstructured data to prepare datasets for downstream analysis and visualization.

The Data Scientist will develop agent-based workflows and AI features for CAS products, emphasizing software development and collaboration with teams.

Lead research and experimentation to measure ad campaign performance and effectiveness. Design evaluation frameworks, build measurement solutions, develop models for lift, conversion, and attribution, run experiments and analyses, partner with product/engineering/design/sales, present insights, mentor teammates, and foster technical community and culture.

Own analytical strategy for enterprise knowledge-worker adoption across ChatGPT Work and Codex. Define measurement frameworks for activation, retention, workflow depth, and monetization. Analyze behavioral and customer evidence to identify personas, use cases, and interventions that drive durable usage. Design experiments and quasi-experiments, operationalize datasets and scorecards, improve instrumentation with Analytics Engineering, and translate insights into recommendations for Product, GTM, Finance, and executives.

Build and maintain forecasting, driver-based models, and analytics for FP&A. Analyze historical and operational drivers, improve forecast accuracy with statistical and ML methods, create dashboards and automated reports, integrate ERP/CRM data, perform variance and scenario analysis, and present insights to finance leaders.

Lead development of data science and ML solutions: explore, clean, analyze structured and unstructured data, build predictive models, create visualizations, guide teammates, and advise clients—often within secure DoD environments.

Work with clients to analyze large, structured and unstructured data sets, design and test AI/ML models, mentor teammates, and deliver actionable insights to leadership, primarily supporting DoW/IC customers.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; partner with product, engineering, and marketing; mentor data scientists; build experimentation frameworks; extract and analyze data using SQL and Python; communicate results to stakeholders.

Lead design, execution, and interpretation of A/B tests and quasi-experiments; apply causal inference methods; build centralized experimentation frameworks; extract and analyze data with SQL and Python; mentor data scientists and communicate results to stakeholders and senior leadership.

Develop and apply valuation analytics and statistical models in marketing, supporting decision-making and budget optimization through advanced statistical techniques and data analysis.

Lead data science for Search & Discovery: analyze customer search interactions, design and measure experiments, build datasets and analytics pipelines, and optimize ML/LLM ranking models to improve relevance and personalization.