Data Scientist Sr - Contractor
Please send your updated resume at karan.bhatia@systemone.com
Data Scientist
Hybrid 3 days Onsite 2 days remote
Pittsburgh PA, Cleveland OH, Strongsville, Birmingham AL, Dallas, TX, Phoenix
Contract
Roles And Responsibilities
• Collaborate with other data scientists, data engineers and DevOps engineers to help build and deploy models using SageMaker in a hybrid environment
• Coordinate the build and automations for the entire MLOps pipeline including data and features, model (re)developments, deployment and ongoing monitoring of inference endpoints and model performance
• Implement automated monitoring and alerting systems to detect and remediate potential issues proactively
• Look for opportunities to optimize timelines, resource utilizations and resiliency of end-to-end MLOps process
• Collaborate for the development and integration of customized LLMs to enhance data analysis, natural language understanding, and generation tasks for agentic systems
• Stay updated on the latest developments, explore and experiment to push boundaries and contribute to team and intellectual property development
Must Have Technical Skills:
• Python and PySpark proficient
• Statistical analysis with data cleaning and augmentation experience
• Strong footing on ML algorithms and their suitability for varied use cases
• Deep learning and NLP experience (TensorFlow/PyTorch, BERT/GPT-3, etc.)
• AWS SageMaker and additional AWS services (Lambda, StepFunctions, etc.)
Flex Skills/Nice to Have:
• Fine-tuning LLMs, SageMaker pipelines, Infrastructure-as-a-code (IaaC), CI/CD, Model Monitoring, Explainable AI (XAI)
Education/Certifications:
• AWS Certified Machine Learning – Specialty
• AWS Certified DevOps Engineer – Professional
• Other Cloud Solution Provider (CSP) certifications in these areas will also count
• Additional Data Science and LLM focused certification will be a plus
Screening Questions:
• Explain MLOps and key components of that in context of AWS SageMaker or similar experience?
• Explain an end-to-end MLOps implementation on SageMaker and if the same had to be implemented in a hybrid state?
• What are some common LLM architectures and explain how they work?
• How would you approach fine-tuning an existing LLM for a specific domain?
• How do you evaluate a model’s performance and specifically what metrics would you use to perform this task for an LLM or a model grounded on an LLM?
Ref: #404-IT Pittsburgh
Please send your updated resume at karan.bhatia@systemone.com
Data Scientist
Hybrid 3 days Onsite 2 days remote
Pittsburgh PA, Cleveland OH, Strongsville, Birmingham AL, Dallas, TX, Phoenix
Contract
Roles And Responsibilities
• Collaborate with other data scientists, data engineers and DevOps engineers to help build and deploy models using SageMaker in a hybrid environment
• Coordinate the build and automations for the entire MLOps pipeline including data and features, model (re)developments, deployment and ongoing monitoring of inference endpoints and model performance
• Implement automated monitoring and alerting systems to detect and remediate potential issues proactively
• Look for opportunities to optimize timelines, resource utilizations and resiliency of end-to-end MLOps process
• Collaborate for the development and integration of customized LLMs to enhance data analysis, natural language understanding, and generation tasks for agentic systems
• Stay updated on the latest developments, explore and experiment to push boundaries and contribute to team and intellectual property development
Must Have Technical Skills:
• Python and PySpark proficient
• Statistical analysis with data cleaning and augmentation experience
• Strong footing on ML algorithms and their suitability for varied use cases
• Deep learning and NLP experience (TensorFlow/PyTorch, BERT/GPT-3, etc.)
• AWS SageMaker and additional AWS services (Lambda, StepFunctions, etc.)
Flex Skills/Nice to Have:
• Fine-tuning LLMs, SageMaker pipelines, Infrastructure-as-a-code (IaaC), CI/CD, Model Monitoring, Explainable AI (XAI)
Education/Certifications:
• AWS Certified Machine Learning – Specialty
• AWS Certified DevOps Engineer – Professional
• Other Cloud Solution Provider (CSP) certifications in these areas will also count
• Additional Data Science and LLM focused certification will be a plus
Screening Questions:
• Explain MLOps and key components of that in context of AWS SageMaker or similar experience?
• Explain an end-to-end MLOps implementation on SageMaker and if the same had to be implemented in a hybrid state?
• What are some common LLM architectures and explain how they work?
• How would you approach fine-tuning an existing LLM for a specific domain?
• How do you evaluate a model’s performance and specifically what metrics would you use to perform this task for an LLM or a model grounded on an LLM?
Ref: #404-IT Pittsburgh
