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.