Data Scientist

Compunnel, Inc.; San Francisco (CA); On-site; USD 120,000 - 150,000; Full time

Job summary

Compunnel, Inc. is seeking a Data Scientist to serve as an AI/ML subject matter expert. Responsibilities include building and maintaining core AI/ML models, advising internal teams, and providing technical support for AI/ML systems.

The ideal candidate has a Master's degree and 4+ years of experience in data science, strong Python skills, and expertise in advanced AI models. This role will contribute to MLOps practices and deploy AI solutions effectively.

Qualifications

• 4+ years in data science, ML engineering, or AI development roles.
• Proven track record building and deploying ML models in production environments.
• Experience processing and extracting insights from unstructured documents at scale.

Responsibilities

• Advise users on modeling approaches and troubleshoot models.
• Develop, maintain and improve CDP owned models.
• Train data engineers and business users on AI/ML best practices.

Skills

Deep expertise in search, information retrieval, and ranking systems

Strong understanding of neural search architectures

Excellent cross-team collaboration and communication skills

Strong Python proficiency

Experience applying LLMs and agentic AI techniques

Education

Master's degree in Data Science, Statistics, Computer Science, Mathematics

Tools

TensorFlow

PyTorch

SQL

R

Stata

Job description

Job Summary

The Data Scientist will serve as an AI/ML subject matter expert, focusing on consulting with internal teams, building and maintaining core AI/ML models, and providing technical support for AI/ML systems. Responsibilities include advising on modeling approaches, developing and deploying AI solutions for use cases, and contributing to the establishment of MLOps practices and GenAI frameworks.

Key Responsibilities

• 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.
• Split 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.
• 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 client's regulatory requirements.

Required Qualifications

• 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, TensorFlow, 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.
• Communication: Ability to explain complex AI concepts to non‑technical stakeholders and translate business problems into technical solutions.

Preferred Qualifications

• Working knowledge of AWS AI/ML services (SageMaker, Bedrock preferred).
• Experience working with our tech stack Databricks, AWS AI/ML tools, Starburst is preferred.