Über
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.
Sprachkenntnisse
- English
Hinweis für Nutzer
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