Data Scientist

Data Scientist – San Francisco, CA

You are invited to apply for the Data Scientist position at Stefanini Group located in San Francisco, CA.

Prakhar Goyal: (248) 263‑5255 /

Open for W2 only!

• 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, Data Mesh 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.

• 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…)
• Develop and deploy 1–2 RAG/knowledge‑base systems in the first year
• Create reusable GenAI frameworks and patterns for the organization
• Implement solutions using AWS AI services (Bedrock, Sage Maker, Textract, Databricks…)
• 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

• Your number one job will be to help advise users on appropriate modeling approaches based on their use cases
• Assist users troubleshoot their models for performance issues (both processing time and accuracy)
• Act as third‑level support for issues related to AI and ML models
• Develop, maintain and improve CDP‑owned models
• Help other support team members advance their knowledge of data science and modeling
• Train the users by providing models and materials to be used for training
• Review CDP architectural design proposals that include the use of AI/Machine Learning/GenAI
• Stay current on modeling techniques and Fed requirements on the use of AI/ML models
• 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

• Education:
  
  Master’s degree in Data Science, Statistics, Computer Science, Mathematics, or related quantitative field
• Experience:
  
  4+ years in data science, ML engineering, or AI development roles
• Production ML:
  Proven track record building and deploying ML/AI models in production environments
• Programming:
  Strong Python proficiency; experience with SQL and at least one statistical language (R, Stata, Matlab, Sparkly R)
• ML Frameworks:
  Hands‑on experience with modern ML frameworks (scikit‑learn, Tensor Flow, PyTorch, Hugging‑Face)
• Generative AI:
  Practical experience with LLMs, RAG architectures, and prompt engineering
• Document AI:
  Experience processing and extracting insights from unstructured documents at scale
• Cloud Platforms:
  Working knowledge of AWS…