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Primary Skill /s - Machine Learning to Customer Marketing initiatives -Python and libraries like PySpark, TensorFlow, PyTorch, and Scikit-learn big data ecosystems (e.g., Hadoop/Spark) and cloud platforms (e.g., Azure, AWS, GCP) Required Skills & Qualifications Solid programming skills in Python with proven experience building production-grade ML systems.

Hands-on expertise in PySpark and distributed data processing for feature engineering and model training.

Practical experience with ML frameworks such as Scikit-learn, and at least one deep learning framework (TensorFlow or PyTorch).

Solid understanding of supervised/unsupervised ML techniques and evaluation metrics (AUC, F1, precision/recall, lift, calibration).

Experience working within big data ecosystems (Hadoop, Spark) and data lake/lakehouse concepts.

Cloud experience in one or more platforms: Azure / AWS / GCP (compute, storage, orchestration, security basics).

Familiarity with MLOps practices: model versioning, reproducibility, CI/CD,



automated testing, and monitoring.

Strong communication skills and ability to explain model outputs to business partners (especially marketing stakeholders).

Preferred / Nice-to-Have Experience with customer marketing analytics: segmentation, personalization, recommender systems, attribution, uplift modeling, MMM, or experimentation.

Knowledge of feature stores, ML pipeline orchestration (Airflow, ADF, Step Functions, Composer), and model registries.

Experience with containerization & orchestration: Docker/Kubernetes.

Exposure to modern data platforms: Databricks, Snowflake, BigQuery, SageMaker, Vertex AI, Azure ML.

Familiarity with data governance, PII handling, consent frameworks, and regulatory requirements (industry dependent

5+
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Apply on Kit Job: kitjob.ca/job/2fsucj

📌 Machine Learning Engineer (Toronto)
🏢 Astra North Infoteck
📍 Toronto