AI Engineering Lead responsible for infrastructure and development of AI agents in product analytics. Managing tools, standards, and governance for a team supporting 60+ products.
About the role
- Junior Data Scientist developing data infrastructure for CaRMS to support decision-making. Engaging in data engineering and data-driven insights for stakeholders in a remote role.
Responsibilities
- Designing, implementing, and operating critical data infrastructure, including systems for updating the corporate Data Warehouse, passing information to and from our matching software, and generating data products (operational reporting, data contracts, match statistics, etc.)
- Migrating ETL associated with passing information to and from the matching software from Informatica PowerCenter to new PostgreSQL/Python-based data platform (PostgreSQL, SQLAlcemy / SQLModel, Dagster, and MkDocs)
- Developing internal matching platform API (using FastAPI) to run ETL associated with the matching software and help application developers use it
- Consolidating overlapping SQL views across data products to ensure consistency
- Developing modular Python-based reporting framework for producing data contracts, operational reporting, and custom data requests
- Maintaining and extending match simulation software and conducting “what-if” scenario analysis for stakeholders in collaboration with the Lead Data Scientist
- Contributing to R markdown/Quarto-based "insight" research pieces for internal and external stakeholders
- Helping our stakeholders understand applicant and employer preferences (preference modeling)
- Developing better ways to help our clients find their ideal candidates/residency positions (for use in our broader web application)
Requirements
- Four-year degree in data science, economics, computer science, engineering, applied mathematics, statistics or equivalent work experience.
- Very strong proficiency in Python and advanced SQL skills is required.
- 3-5 years of experience with Python-based data engineering / data science packages (particularly SQLAlchemy/SQLModel, pandas, Dagster, FastAPI, and LangChain).
- Experience using cloud data storage (AWS S3), PostgreSQL-compatible database services (i.e., fully managed through RDS / Aurora, or self-managed on Amazon EC2), and compute (EC2, ECS, Fargate) is very highly valued.
- Significant experience with relational database systems (e.g., Oracle, PostgreSQL, etc.).
- Deep understanding of data management concepts associated with designing, building, maintaining, and extending an Enterprise Data Warehouse.
- Use of version control (Git) and test-based development practices should be strongly engrained in your workflow.
- Practical experience with any of the following is valued: Implementing semantic search and Q&A on documents, Computational statistics, particularly resampling techniques, Matching algorithms, Informatica PowerCenter, Using Quarto/R markdown to produce reproducible reporting, Developing and supporting dashboards (e.g., Tableau, MS Power BI, etc.), Jira and Confluence collaboration tools.
Benefits
- Professional growth opportunities
- Comprehensive benefits package
- Defined contribution pension plan
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