Senior Data Scientist - Predictive Modeling & Machine Learning

Remote from PST - Please confirm if you are fine with this timezone

Please provide the LinkedIn when submitting the resume.

MUST HAVE OIL AND GAS, UTILITY OR PIPELINE INDUSTRY EXPERIENCE

Important must have Predictive Modeling & Machine Learning (with data engineering) as well as RELEVANT Utilities AI/ML development experience.

We are seeking a highly motivated and experienced Senior Data Scientist with Data Engineering skills to join our dynamic team. This role will focus on developing and deploying advanced AI/ML predictive models to drive key business decisions. The ideal candidate will possess a strong background in machine learning, data engineering, and cloud computing, with a proven track record of delivering impactful solutions. You will leverage AWS services, particularly SageMaker, Bedrock, LLM, PyTorch, Tensorflow, Deeplearning containers, Jupyter notebooks and Glue, to build scalable and efficient data pipelines and machine learning models.

Responsibilities:

• Predictive Modeling & Machine Learning:
• Develop and implement advanced machine learning models for predictive analytics, forecasting, and optimization.
• Train, evaluate, and deploy machine learning models using AWS SageMaker, Bedrock, LLM, PyTorch, Tensorflow, Deeplearning containers, Jupyter notebooks and Glue.
• Conduct thorough model validation and performance monitoring.
• Translate complex data insights into actionable business recommendations.
• Data Engineering & AWS:
• Design and implement robust data pipelines using AWS Glue for data extraction, transformation, and loading (ETL).
• Manage and optimize data storage and processing in the AWS cloud environment.
• Ensure data quality and integrity throughout the data lifecycle.
• Utilize other AWS services as needed to enhance data processing and model deployment.
• Python Programming:
• Develop and maintain Python scripts for data manipulation, analysis, and model implementation.
• Write clean, efficient, and well-documented code.
• Utilize relevant Python libraries (e.g., pandas, scikit-learn, TensorFlow, PyTorch).
• Collaboration & Communication:
• Collaborate with cross-functional teams, including engineers, business analysts, and stakeholders.
• Communicate complex technical concepts effectively to both technical and non-technical audiences.
• Present findings and recommendations to senior management.
• (Optional) Utility Specific: Apply machine learning to solve problems specific to the utility industry (e.g., demand forecasting, grid optimization, asset management).
• (Optional) Generative AI: Explore and implement Generative AI techniques to enhance existing models and develop new solutions.
• (Optional) GIS/Geospatial Data: Utilize geospatial data and GIS tools to enhance predictive models and provide location-based insights.