Senior Data Scientist
One of CEI's largest Financial Services & Banking clients is seeking a Sr. Data Scientist to join their growing organization!
Client/Industry: Financial Services & Banking
Job Title: Senior Data Scientist & MLOps Engineer
Location: Hybrid - 3 Days On-Site / 2 Days Remote | Pittsburgh, PA 15222 or Cleveland, OH 44136
Work Schedule/Shift: Mon-Fri | Minimum 40 work hours per week.
Duration/Length of Assignment: 12 Month Contract to Hire
• Must be able to convert to a full-time employee without sponsorship, restrictions, or an additional employer
•
• W2 Employment Only – No Corp to Corp / C2C arrangements.
• Expected potential for contract extension(s) and/or conversion to Full-Time/Permanent Employment.
• Optional benefits available during contract (Medical, Dental, Vision, and 401k)
Position Overview:
This role is part of a newly expanding Data Science team focused on developing, deploying, and scaling machine learning (ML) and artificial intelligence (AI) solutions for business-critical systems. The position supports enterprise initiatives centered around optimizing ML pipelines, leveraging large-scale data, and integrating advanced analytics to support agentic systems and automated decision-making frameworks. The team consists of data scientists, MLOps engineers, and DevOps engineers collaborating in a hybrid cloud environment to modernize and scale AI/ML models and services. The team operates under a collaborative structure and cross-functional model, working closely with engineering, product, and DevOps groups. The selected candidate will be responsible for designing and implementing end-to-end ML workflows, deploying scalable inference endpoints, customizing large language models (LLMs), and automating monitoring solutions to support continuous delivery and operational integrity. They will lead efforts to develop pipelines and infrastructure for ML model training, testing, and production deployment, while also contributing to research-based improvements in ML approaches and data-driven features.
Required Skills/Experience/Qualifications:
• Master’s degree in Data Science, Computer Science, Engineering, Applied Mathematics, or related field
• 6+ years of experience with Python and PySpark
• 6+ years of hands-on experience with AWS SageMaker and other AWS services (e.g., Lambda, Step Functions)
• 6+ years of deep learning and NLP experience with TensorFlow, PyTorch, and transformer-based models (e.g., BERT, GPT-3)
• 6+ years of experience in statistical analysis, data cleaning, and augmentation
• Strong understanding of machine learning algorithms and their applications across various use cases
• Minimum of 5 years architecting and deploying machine learning solutions in hybrid environments
• Proven experience with LLMs and related architectures, including experience in Retrieval Augmented Generation (RAG) and agentic systems
• Strong programming knowledge for API-driven microservices and inference endpoints
Preferred Skills (Not Required):
• AWS Certified Machine Learning – Specialty or AWS Certified DevOps Engineer – Professional
• Experience with SageMaker Pipelines and Infrastructure-as-Code (IaC)
• Familiarity with CI/CD processes, Explainable AI (XAI), and model monitoring frameworks
• Additional certifications or coursework related to data science, cloud architecture, or LLM development
Day to Day/Responsibilities:
• Collaborate with data scientists, data engineers, and DevOps engineers to deploy and manage models in a hybrid cloud environment using AWS SageMaker
• Lead the development and automation of the entire MLOps pipeline, including data preparation, feature engineering, model training, deployment, and continuous monitoring
• Develop monitoring and alerting systems to proactively identify and address potential model performance issues or infrastructure failures
• Manage automated model retraining, endpoint health, and resource allocation to improve model availability and pipeline efficiency
• Design and implement custom LLM integrations to support advanced NLP use cases and enhance system intelligence for agentic workflows
• Apply statistical methods for data transformation, validation, and augmentation in preparation for ML pipeline inclusion
• Utilize Python, PySpark, and cloud-based tools to analyze structured and unstructured data across various domains
• Use tools like TensorFlow, PyTorch, BERT, and GPT-3 for deep learning tasks and transformer-based modeling
• Develop and maintain microservice APIs to facilitate inference endpoint integration and real-time prediction services
• Maintain compliance with CI/CD and IaC standards for deploying ML solutions in a secure, scalable, and consistent manner
• Engage in regular cross-functional team meetings to provide updates on modeling progress, pipeline improvements, and monitoring outcomes
• Stay current with evolving trends in ML, LLMs, Explainable AI (XAI), and cloud-based deployments to contribute to innovation and team capabilities
One of CEI's largest Financial Services & Banking clients is seeking a Sr. Data Scientist to join their growing organization!
Client/Industry: Financial Services & Banking
Job Title: Senior Data Scientist & MLOps Engineer
Location: Hybrid - 3 Days On-Site / 2 Days Remote | Pittsburgh, PA 15222 or Cleveland, OH 44136
Work Schedule/Shift: Mon-Fri | Minimum 40 work hours per week.
Duration/Length of Assignment: 12 Month Contract to Hire
• Must be able to convert to a full-time employee without sponsorship, restrictions, or an additional employer
•
• W2 Employment Only – No Corp to Corp / C2C arrangements.
• Expected potential for contract extension(s) and/or conversion to Full-Time/Permanent Employment.
• Optional benefits available during contract (Medical, Dental, Vision, and 401k)
Position Overview:
This role is part of a newly expanding Data Science team focused on developing, deploying, and scaling machine learning (ML) and artificial intelligence (AI) solutions for business-critical systems. The position supports enterprise initiatives centered around optimizing ML pipelines, leveraging large-scale data, and integrating advanced analytics to support agentic systems and automated decision-making frameworks. The team consists of data scientists, MLOps engineers, and DevOps engineers collaborating in a hybrid cloud environment to modernize and scale AI/ML models and services. The team operates under a collaborative structure and cross-functional model, working closely with engineering, product, and DevOps groups. The selected candidate will be responsible for designing and implementing end-to-end ML workflows, deploying scalable inference endpoints, customizing large language models (LLMs), and automating monitoring solutions to support continuous delivery and operational integrity. They will lead efforts to develop pipelines and infrastructure for ML model training, testing, and production deployment, while also contributing to research-based improvements in ML approaches and data-driven features.
Required Skills/Experience/Qualifications:
• Master’s degree in Data Science, Computer Science, Engineering, Applied Mathematics, or related field
• 6+ years of experience with Python and PySpark
• 6+ years of hands-on experience with AWS SageMaker and other AWS services (e.g., Lambda, Step Functions)
• 6+ years of deep learning and NLP experience with TensorFlow, PyTorch, and transformer-based models (e.g., BERT, GPT-3)
• 6+ years of experience in statistical analysis, data cleaning, and augmentation
• Strong understanding of machine learning algorithms and their applications across various use cases
• Minimum of 5 years architecting and deploying machine learning solutions in hybrid environments
• Proven experience with LLMs and related architectures, including experience in Retrieval Augmented Generation (RAG) and agentic systems
• Strong programming knowledge for API-driven microservices and inference endpoints
Preferred Skills (Not Required):
• AWS Certified Machine Learning – Specialty or AWS Certified DevOps Engineer – Professional
• Experience with SageMaker Pipelines and Infrastructure-as-Code (IaC)
• Familiarity with CI/CD processes, Explainable AI (XAI), and model monitoring frameworks
• Additional certifications or coursework related to data science, cloud architecture, or LLM development
Day to Day/Responsibilities:
• Collaborate with data scientists, data engineers, and DevOps engineers to deploy and manage models in a hybrid cloud environment using AWS SageMaker
• Lead the development and automation of the entire MLOps pipeline, including data preparation, feature engineering, model training, deployment, and continuous monitoring
• Develop monitoring and alerting systems to proactively identify and address potential model performance issues or infrastructure failures
• Manage automated model retraining, endpoint health, and resource allocation to improve model availability and pipeline efficiency
• Design and implement custom LLM integrations to support advanced NLP use cases and enhance system intelligence for agentic workflows
• Apply statistical methods for data transformation, validation, and augmentation in preparation for ML pipeline inclusion
• Utilize Python, PySpark, and cloud-based tools to analyze structured and unstructured data across various domains
• Use tools like TensorFlow, PyTorch, BERT, and GPT-3 for deep learning tasks and transformer-based modeling
• Develop and maintain microservice APIs to facilitate inference endpoint integration and real-time prediction services
• Maintain compliance with CI/CD and IaC standards for deploying ML solutions in a secure, scalable, and consistent manner
• Engage in regular cross-functional team meetings to provide updates on modeling progress, pipeline improvements, and monitoring outcomes
• Stay current with evolving trends in ML, LLMs, Explainable AI (XAI), and cloud-based deployments to contribute to innovation and team capabilities
