Data Scientist

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.

Due to Security clearance needed to obtain must be a US Citizen

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 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.

Due to Security clearance needed to obtain must be a US Citizen

#LI-MN1

qualifications: 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.

Due to Security clearance needed to obtain must be a US Citizen

Equal Opportunity Employer: Race, Color, Religion, Sex, Sexual Orientation, Gender Identity, National Origin, Age, Genetic Information, Disability, Protected Veteran Status, or any other legally protected group status.

At Randstad Digital, we welcome people of all abilities and want to ensure that our hiring and interview process meets the needs of all applicants. If you require a reasonable accommodation to make your application or interview experience a great one, please contact HRsupport@randstadusa.com.

Pay offered to a successful candidate will be based on several factors including the candidate's education, work experience, work location, specific job duties, certifications, etc. In addition, Randstad Digital offers a comprehensive benefits package, including: medical, prescription, dental, vision, AD&D, and life insurance offerings, short-term disability, and a 401K plan (all benefits are based on eligibility).

This posting is open for thirty (30) days.

It is unlawful in Massachusetts to require or administer a lie detector test as a condition of employment or continued employment. An employer who violates this law shall be subject to criminal penalties and civil liability.

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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.

Due to Security clearance needed to obtain must be a US Citizen

#LI-MN1

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 re