Data Scientist

apartmentStefanini, Inc placeSan Francisco calendar_month5/1/26

Stefanini Group is hiring!

Stefanini is looking for a Data Scientist in San Francisco, CA

For quick Apply, please reach out to Ayush Dwivedi; 248 728 2636/
Ayush.dwivedi

@stefanini.com

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

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