Data Scientist & ML Engineer: Production AI & Insights

Donuts Inc.; Toronto; On-site; CAD 87,000 - 114,000; Full time

Job summary

Identity Digital is seeking a Data Scientist & Machine Learning Engineer to join the Data & Analytics team. You will translate business problems into data-driven ML solutions, build actionable models, and deploy them in production.

Responsibilities include data collection, modeling, feature engineering, and maintaining ML pipelines across batch and real-time inference. Collaboration with engineers and domain experts is essential.

Qualifications

• Bachelor's Degree in Mathematics, Computer Science, Data Science, Engineering or other quantitative field is required.
• Advanced degree (Masters or PhD) in AI/ML preferred.

Responsibilities

• Translate complex business problems into data, modeling, and machine-learning solutions.
• Collect, clean, transform, and validate data for analysis and model development.
• Explore data to identify patterns, trends, anomalies, and actionable insights.
• Design, train, evaluate, and refine predictive or optimization models.
• Select appropriate features, algorithms, metrics, and validation strategies.
• Build reproducible data and machine-learning pipelines.
• Deploy models for batch or real-time inference.
• Develop and maintain infrastructure for reliable model serving.
• Monitor model performance, data quality, drift, latency, and operational health.
• Communicate findings and collaborate with stakeholders, engineers, and domain experts.

Skills

Python

SQL

Data analysis

ML concepts

Education

Bachelor's degree in quantitative field

Advanced degree preferred

Tools

Airflow

Kubernetes

BigQuery

Databricks

Snowflake

PyTorch

TensorFlow

Pandas

Polars

Job description

Identity Digital is seeking a Data Scientist & Machine Learning Engineer to join the Data & Analytics team. You will translate business problems into data-driven ML solutions, build actionable models, and deploy them in production.

Responsibilities include data collection, modeling, feature engineering, and maintaining ML pipelines across batch and real-time inference. Collaboration with engineers and domain experts is essential.