job summary:
The Common Data Platform (CDP) team manages 100TB+ of data from across the client, serving economists, executives, and policy makers. We're adding AI/ML capabilities to transform how our organization extracts insights from documents, detects anomalies, and empowers decision-making.
As our Data Scientist, 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.
Required 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
- Cloud Platforms: Working knowledge of AWS AI/ML services (SageMaker, Bedrock preferred)
- Communication: Ability to explain complex AI concepts to non-technical stakeholders and translate business problems into technical solutions
- Tooling: Experience working with our tech stack Databricks, AWS AI/ML tools, Starburst is preferred
Job Duties - 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
Job Requirements - The Common Data Platform (CDP) team manages 100TB+ of data from across the Federal Reserve, serving economists, executives, and policy makers. We're adding AI/ML capabilities to transform how our organization extracts insights from documents, detects anomalies, and empowers decision-making.
As our Data Scientist, 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
Minimum Qualifications
- 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
- Cloud Platforms: Working knowledge of AWS AI/ML services (SageMaker, Bedrock preferred)
- Communication: Ability to explain complex AI concepts to non-technical stakeholders and translate business problems into technical solutions
- Tooling: Experience working with our tech stack Databricks, AWS AI/ML tools, Starburst is preferred
Desired Skills & Experience - Experience working with our tech stack Databricks, AWS AI/ML tools, Starburst is preferred.
location: Telecommute
job type: Solutions
salary: $80 - 110 per hour
work hours: 9am to 5pm
education: Bachelors
responsibilities:
The Common Data Platform (CDP) team manages 100TB+ of data from across the client, serving economists, executives, and policy makers. We're adding AI/ML capabilities to transform how our organization extracts insights from documents, detects anomalies, and empowers decision-making.
As our Data Scientist, 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.
Required 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 dep
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