Sr. Data Scientist II

Sr. Data Scientist II

Requirements

• Bachelor’s degree plus 7-10 years of data science experience, or a Masters Degree plus 5 years of data science experience.
• Active Top Secret clearance with the ability to obtain a SCI
• Experience with ML fields, e.g., natural language processing, computer vision, statistical learning theory
• Hands-on experience with Natural Language Processing (NLP), Large Language Models, text embedding, semantic query, use of generative AI for text, and retrieval augmented generation (RAG)
• Familiarity with data preprocessing, feature engineering, and model evaluation techniques essential for machine learning projects
• Strong understanding of various machine learning algorithms, including supervised and unsupervised learning, reinforcement learning, and neural networks
• Experience with version control systems like Git, enabling effective collaboration and code management
• Experience in an ML engineer or data scientist role building ML models
• Experience writing code in Python, R, Scala, Java, C++ with documentation for reproducibility
• Experience using Apache Spark/Databricks distributed compute environments for AI/ML workloads
• Experience handling petabyte size datasets, diving into data to discover hidden patterns, using data visualization tools, writing SQL, and working with GPUs to develop models
• Experience with cloud-based data persistence products, especially RDS PostgreSQL and PostgreSQL extensions such as pgvector.
• Experience writing and speaking about technical concepts to business, technical, and lay audiences and giving data-driven presentations.

Responsibilities

• Designs, configures, develops, tests, and supports informatics and data science solutions for a wide array of technical use cases;
• Collaborate with cross-functional teams, including data scientists and software engineers to integrate AI solutions developed by other elements of CDAO or the DoD community into Search Portfolio products when appropriate
• Optimize AI models for performance, scalability, and efficiency, leveraging cloud-based resources and distributed computing frameworks, specifically Apache Spark/Databricks. Ability to adapt code base to also run using GPU enabled Kubernetes clusters.
• Stay updated on and contribute to the latest advancements in AI research, applying new findings to improve Search Portfolio products
• Manage the lifecycle of AI/ML components used in Search Portfolio products from research and development to deployment and optimization
• Applies analytical methodologies to diagnose data-related challenges, implement solutions, and evaluate performance;
• Documents and presents requirements, design alternatives, and findings to team members and clients;
• Ability to develop strategic, baselined, data modeling processes; ability to accurately determine cause-and-effect relationships; and
• Maintains and guides the development of common libraries and tools used by multiple teams.
• Aids in formulating a strategy on how to achieve rapid prototyping.

Preferred Qualifications

No preferred qualifications provided.