- Company Name
- ARC Resources Ltd.
- Job Title
- Data Science Engineer/Developer
- Job Description
-
Job Title: Data Science Engineer/Developer
Role Summary
Data Science Engineer/Developer responsible for designing, building, validating, and productionizing machine learning models and pipelines. Works closely with analytics, engineering, and business stakeholders to deliver scalable data solutions that drive business decisions across multiple operational domains.
Expectations
- Deliver high‑quality, reproducible models that meet business objectives and maintain rigorous governance and performance tracking.
- Operate within an MLOps framework, ensuring continuous integration, automated testing, and reliable deployment of models to production environments.
- Communicate technical insights plainly to business stakeholders and support adoption of solutions within existing workflows.
Key Responsibilities
- Design, implement, train, and validate predictive models (regression, classification, time‑series, deep learning) using Python and PySpark.
- Prototype and evaluate emerging machine learning techniques to propose innovative solutions.
- Monitor model drift, performance, and usage; enforce governance standards and maintain detailed documentation.
- Build, manage, and optimize CI/CD pipelines for ML model packaging, testing, and promotion using Git, Docker, and orchestration tools.
- Deploy models and data pipelines on Databricks, and configure deployment to Azure/AWS/GCP as required.
- Collaborate with data engineering teams to ensure high‑quality data availability for analytics and reporting.
- Assess ROI of system components, simplifying workflows for maintainability and scalability.
Required Skills
- 5+ years of architecting enterprise‑scale data and ML solutions and 3+ years of productionizing models following MLOps best practices.
- Proficiency in Python, PySpark, data structures, and algorithms.
- Experience with MLflow or equivalent for model tracking, registry, and deployment.
- Hands‑on experience building real‑time/batch pipelines in Databricks; knowledge of Azure, AWS, or GCP optional.
- Strong version control skills (Git) and CI/CD practices for ML workflows.
- Excellent written and verbal communication; ability to translate technical findings into actionable business insights.
Required Education & Certifications
- Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Engineering, or a related field.
- Relevant certifications in data engineering, cloud platforms (Azure, AWS, GCP), or MLOps practices are advantageous.