Data Scientist, Databricks, Mosaic AI

🔥 2 hours ago

⚔️ Virginia – Remote

⚔️ Virginia – Remote

💵 $110k - $140k / year

⏰ Full Time

🟡 Mid-level

🟠 Senior

📊 Data Scientist

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OPENDataJobs

WebsiteLinkedInAll Job Openings

2 - 10 employees

Founded 2024

🎯 Recruiter

🏛️ Government

Recruitment • Government

OPENDataJobs is a specialized recruitment and career platform that connects AI, data, and software professionals with federal-sector opportunities. The site publishes live open positions drawn from its recruiting system and organizes roles into a standardized taxonomy (seven categories and twenty-five roles) with role pages describing work, key skills, and career paths. OPENDataJobs focuses on federal hires across agencies, contractors, consulting firms, and technology providers, handles applications through a secure recruiting system, and is a division of Peregrine Advisors Benefit, Inc.

📋 Description

• Build, evaluate, and put machine learning and generative AI models into production for a federal agency • Develop and train machine learning models on Azure Databricks • Track experiments, models, and versions in MLflow • Build reusable features in the Databricks Feature Store • Serve models through Databricks Model Serving • Build retrieval and agent applications with Agent Bricks and Databricks AI Search using Azure OpenAI models through Microsoft Foundry • Evaluate model and agent quality with MLflow evaluation, LLM judges, labeled test sets, and human review • Monitor production models for drift and quality • Write documentation requested by model reviews • Measure model quality before and after release • Record each model's purpose and limitations • Leave maintainable pipelines that teams can retrain and rerun • Collaborate with developers, engineers, data scientists, architects, and strategists across strategy and implementation work • Help develop the firm's capabilities, tools, and lines of business

🎯 Requirements

• 4+ years of data science or machine learning engineering experience, including models deployed to production • Bachelor's degree • Python for data science and machine learning, including pandas, scikit-learn, and PySpark • Hands-on Azure Databricks work, including MLflow for experiment tracking and model management • Deploying and monitoring models in production, including model serving, versioning, and drift monitoring • Building LLM applications, including retrieval-augmented generation (RAG) • Evaluating model and LLM output against defined metrics, test sets, and human review • Structured Query Language (SQL) and Git-based version control • Delivery experience in a federal government environment, working to its security, access, and review processes • Proficiency in writing, PowerPoint, and Excel • United States citizenship; dual citizenship and green card holders are prohibited by the contract terms • Ability to satisfy the required suitability, security, and access requirements • Preferred: Experience on a federal system accredited at Impact Level 5 (IL-5) or higher • Preferred: Agent Bricks, Databricks AI Search (formerly Vector Search), or MLflow 3 evaluation for generative AI • Preferred: Experience with a second managed machine-learning platform, such as Azure Machine Learning, Amazon SageMaker, or Google Vertex AI • Preferred: Azure OpenAI or other models in Microsoft Foundry • Preferred: Databricks certification, or a Microsoft Azure AI or data science certification • Preferred: Model risk management or model validation experience, including documentation a model review asks for • Preferred: Familiarity with artificial intelligence (AI)-assisted developer tooling

🏖️ Benefits

• Medical, dental, and vision with the employee premium fully paid and half of dependent premiums • Employer-paid life, accidental death, and short-term and long-term disability insurance • 401(k) with an employer match of up to 4% of salary • Unlimited paid time off • Sponsored professional certifications and continuing education • Extensive onboarding • Rotation across functions to expand skills and perspective

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