Data Scientist & Machine Learning Engineer

Summary / Objective

The Data & Analytics team builds and manages data systems, analyzes information to generate insights, and develops machine learning models to support automated decisioning. As a Data Scientist and Machine Learning Engineer, you will work across (and connect) the expertise of these two related but distinct disciplines. Your responsibilities will include (but not be limited to): deriving data insights to drive decision making, applying ML and modeling techniques to solve business problems, and deploying and managing those models in production.

This role reports to the Director of Business Intelligence.

What You'll Do

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
Actively models and promotes Identity Digital’s core values through day-to-day interactions, behaviors, and decision-making
Other duties as assigned
Who You Are / What You Bring

3+ years of experience in developing, deploying, and monitoring production data / ML pipelines
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
Deep technical knowledge in several of the following topics: anomaly detection, time-series analysis, structured and unstructured data processing, causal inference, reinforcement learning, embeddings and vector databases, large language models, and modern MLOps / LLMOps practices
Professional development experience with the following tools and platforms: Airflow, Argo Workflows, cloud data warehouses (BigQuery, Databricks, Snowflake), major cloud providers (AWS, GCP), Git, Kubernetes, MLFlow, Pandas, Polars, PyTorch, TensorFlow, SHAP and leveraging AI / LLMs.
Strong programming proficiency in Python and SQL; familiarity with Shell scripting is a plus
Exceptional problem-solving skills with the ability to diagnose and resolve issues from limited or ambiguous information
Creative and strategic thinker with a strong analytical foundation and a focus on measurable impact
Collaborative communicator who excels in cross-functional environments and builds strong working relationships
Highly organized with excellent time management, attention to detail, and execution discipline
Knowledge of the domain name industry or digital identity ecosystem is a plus
Ability to travel as needed
Ability to work across time zones as part of a global organization as needed
Reliable transportation to the workplace
Physical Requirements

Prolonged periods of sitting at a desk and working on a computer
Must be able to lift up to 15 pounds at times
Salary Range

The base salary range for this full-time position is CAD $87,000 - $114,000 (flexibility based on experience) plus additional benefits. In addition, the successful candidate will be eligible to receive other compensation from time to time in the form of discretionary and/or nondiscretionary bonuses and long-term incentive plan. Actual compensation will be influenced by a candidate’s qualifications, internal employee equity considerations, and location. We will not ask for information about a candidate's current or past compensation for purposes of developing an offer of employment.

Note: Benefits programs are subject to eligibility requirements and may vary in certain locations.