Data Scientist

Stefanini Group • San Francisco, CA

10h

About The Position

Stefanini Group is hiring! Stefanini is looking for a Data Scientist in San Francisco, CA For quick Apply, please reach out to Prakhar Goyal: [phone]/ [email protected] Open for W2 only! Responsibilities: you'll be the AI/ML subject matter expert, splitting your time between: 50% - Consulting with internal teams (economists, analysts) to design and implement AI solutions for their use cases 25% - Building and maintaining CDP's core AI/ML models and frameworks 25% - Providing technical support and troubleshooting for AI/ML systems You'll work in a collaborative environment using cutting-edge technologies including Databricks, AWS, Collibra, DataMesh architecture, and PySpark to build scalable, production-ready AI systems. This is a foundational role - you'll establish our MLOps practices, GenAI frameworks, and production AI capabilities from the ground up in a highly regulated Federal environment. What You'll Bring Consulting & Enablement (50%) Your number one job will be to help advise economists and business teams on appropriate modeling approaches based on their use cases Advise on appropriate modeling approaches for diverse scenarios: RAG/knowledge bases, anomaly detection, document understanding, audit analysis Bridge the gap between econometric models (R, Stata) and production ML pipelines Review and provide feedback on AI/ML architectural proposals Train data engineers and business users on AI/ML best practices Model Development (25%) Build production-ready AI systems for document processing (PDFs, XLSX, DOCX, CSV etc.,) Develop and deploy 1-2 RAG/knowledge base systems in first year Create reusable GenAI frameworks and patterns for the organization Implement solutions using AWS AI services (Bedrock, SageMaker, Textract, Databricks etc.,) Ensure models meet explainability requirements for regulated environments MLOps & Support (25%) Establish MLOps framework and model deployment patterns Troubleshoot model performance issues (accuracy, latency, cost) Act as escalation point for AI/ML technical issues Train the Users by providing models and documentation as well as consulting Monitor and maintain production models Stay current on AI/ML techniques and Federal regulatory requirements Help other Support Team members advance their knowledge of Data Science and modeling

Requirements

• Deep expertise in search, information retrieval, and ranking systems at scale
• Strong understanding of neural search architectures, ML/AI, and generative models
• ML model development, implementation, and evaluation
• Experience in applying LLMs and agentic AI techniques to production systems
• Demonstrated ability to translate technical solutions into business impact
• Excellent cross-team collaboration and communication skills
• Master's degree in Data Science, Statistics, Computer Science, Mathematics, or related quantitative field
• 4+ years in data science, ML engineering, or AI development roles
• Proven track record building and deploying ML/AI models in production environments
• Strong Python proficiency; experience with SQL and at least one statistical language (R, Stata, Matlab, Sparkly R)
• Hands-on experience with modern ML frameworks (scikit-learn, TensorFlow, PyTorch, Hugging Face)
• Practical experience with LLMs, RAG architectures, and prompt engineering
• Experience processing and extracting insights from unstructured documents at scale
• Working knowledge of AWS AI/ML services (SageMaker, Bedrock preferred)
• Ability to explain complex AI concepts to non-technical stakeholders and translate business problems into technical solutions
• Experience working with our tech stack Databricks, AWS AI/ML tools, Starburst is preferred

Responsibilities

• Consulting with internal teams (economists, analysts) to design and implement AI solutions for their use cases
• Building and maintaining CDP's core AI/ML models and frameworks
• Providing technical support and troubleshooting for AI/ML systems
• Advise economists and business teams on appropriate modeling approaches based on their use cases
• Advise on appropriate modeling approaches for diverse scenarios: RAG/knowledge bases, anomaly detection, document understanding, audit analysis
• Bridge the gap between econometric models (R, Stata) and production ML pipelines
• Review and provide feedback on AI/ML architectural proposals
• Train data engineers and business users on AI/ML best practices
• Build production-ready AI systems for document processing (PDFs, XLSX, DOCX, CSV etc.,)
• Develop and deploy 1-2 RAG/knowledge base systems in first year
• Create reusable GenAI frameworks and patterns for the organization
• Implement solutions using AWS AI services (Bedrock, SageMaker, Textract, Databricks etc.,)
• Ensure models meet explainability requirements for regulated environments
• Establish MLOps framework and model deployment patterns
• Troubleshoot model performance issues (accuracy, latency, cost)
• Act as escalation point for AI/ML technical issues
• Train the Users by providing models and documentation as well as consulting
• Monitor and maintain production models
• Stay current on AI/ML techniques and Federal regulatory requirements
• Help other Support Team members advance their knowledge of Data Science and modeling

What This Job Offers

• Job Type: Full-time
• Career Level: Mid Level