Intermediate Data Scientist to create machine learning algorithms and deep learning models within an AWS environment

Our downtown Oil and Gas Calgary client is seeking an Intermediate Data Scientist to create machine learning algorithms and deep learning models within an AWS environment. This is an initial 1-year contract where the successful candidate will follow an in-office hybrid working model (3-days in office/week).

Must-Haves:

• 5+ years' experience as a data scientist completing statistical analysis and creating data models
• 2+ years' experience creating Machine Learning algorithms and frameworks
• Experience applying deep learning models and methods in Machine Learning
• Python programming experience (creation of data models, statistical analysis, and similar) and a strong programming background

Nice-to-Haves:

• AWS Cloud experience (preferred) or AWS Cloud certification
• SQL or Postgre SQL database experience (ideally AWS Aurora PostgreSQL warehouses) leveraging ETL processes
• Working knowledge of big data solutions (i.e., Hadoop, Spark, Elastic Map Reduce, etc.) 
• Knowledge of AWS Lambda, Amazon Redshift, AWS SageMaker, AWS CI/CD pipelines, AWS Data Lake, AWS DynamoDB

Responsibilities and Scope Overview:

• Provide advanced expertise on statistical and mathematical concepts for the broader Data and Analytics department.
• Support the roll-out of Big Data capabilities through statistical analysis, creation of algorithms, and constructing data models in Python.
• Perform the ingestion of P6 databases into AWS Data Lake and AWS Redshift Warehouses through ETL processes using Python, SQL, and AWS DynamoDB, and AWS Lambda.
• Transform raw data into AWS Aurora PostgreSQL warehouses.
• Work with Data Engineers and SMEs to understand and transform raw data into AWS Aurora PostgreSQL warehouses and/or create engineered features that improve model performance.
• Collaborate with cross-functional team members to deliver high-impact scalable and sustainable products in a Minimum Viable Product (MVP) approach through product releases.
• Use data science techniques to find data patterns, anomalies, and optimization opportunities.