Top Software Data Science Jobs

Develop advanced data science, analytics, machine learning, and predictive modeling solutions for national security and Intelligence Community missions. Analyze large datasets, identify relationships and trends, build computational models, create visualizations and dashboards, and maintain Python-based tools. Collaborate with analysts, engineers, and mission stakeholders while supporting research, documentation, and evolving mission priorities.

Develop and optimize machine learning models using real-world data. Design graph-based ML algorithms, apply research breakthroughs, fine-tune LLMs within RAG pipelines, and develop supervised learning models. Ensure AI explainability, ethics, scalability, and performance while collaborating with development teams. Understand business requirements, coordinate ML development, communicate results visually, and identify new machine learning applications.

Develop mathematical and statistical models, high-performance algorithms, data processing pipelines, and betting-operation automation. Acquire, clean, analyze, and interpret complex datasets; produce reports; identify trends and process improvements; and build, test, and deliver software. The role also involves back-testing, simulations, and collaboration with mathematicians, data scientists, and software engineers.

Research, build, evaluate, and deploy NLP, machine learning, deep learning, and Generative AI solutions for commercial document and revenue lifecycle products. Develop LLM applications, data pipelines, embeddings, vector search, and evaluation frameworks while optimizing models for accuracy, latency, cost, and reliability. Partner with engineering, product, and data science teams to productionize solutions, monitor model performance, conduct applied research, and mentor technical colleagues.

Leads Veeva’s Global Analytics data science function, managing data scientists, AI/ML engineers, and analysts. Translates AI strategy into delivery plans, oversees advanced analytics, machine learning, and generative AI solutions, and establishes global standards for model management, testing, observability, deployment, data quality, governance, security, and responsible AI. Partners with consulting, engineering, product, and business stakeholders to deliver scalable AI-driven solutions for Life Sciences customers.

Analyzes complex, large-scale data to identify trends and organizational problems; applies statistical analysis, data mining, aggregation, and modeling techniques; prepares datasets, develops databases and data models, creates data-driven business solutions, and communicates findings to organizational stakeholders. Supports the Department of State’s Directorate of Passport Services.

Design and maintain scalable data pipelines, architectures, ingestion workflows, transformations, and feature engineering. Partner with data scientists, engineers, architects, and business stakeholders to translate use cases into technical solutions. Apply marketing analytics, KPI design, measurement frameworks, segmentation, experimentation, and statistical analysis. Present actionable insights to executives, communicate complex findings clearly, mentor junior team members, and manage competing project priorities in a client-facing consulting environment.

Develops meteorological data visualizations, statistical analyses, prototypes, and validation processes for satellite, NWP, and AI weather models. The role partners with scientific, engineering, product, marketing, and customer teams to troubleshoot data and model performance, strengthen use cases, and communicate weather insights. Responsibilities include working with proprietary satellite data, operationalizing AI-assisted scientific workflows, integrating diverse meteorological formats, and supporting customer-focused applications of weather intelligence.

Analyze behavioral data, evaluate product experiments, model user behavior, develop dashboards and forecasts, ensure tracking quality, and provide insights that influence product decisions. Collaborate with product and engineering teams while applying statistical, analytical, and AI methods to improve user experiences and business outcomes.

Owns end-to-end clinical research and real-world evidence data management, including research database and EDC configuration, EHR data extraction, transformation pipelines, data cleaning, quality control, data lock, statistical analysis, documentation, and automated EHR-to-EDC integrations. The role requires hands-on work with messy clinical data, scalable ETL solutions, APIs, data models, reproducible code, and cross-functional collaboration with site IT, informatics, product, and research stakeholders.