Team Lead, ML Ops

Who you are  Geotab is seeking a Team Lead, ML Ops who will mentor and coach a team that is responsible for building, implementing, testing, and maintaining scalable data / ML pipelines.
What you’ll do  The Team Lead, ML Ops will take research outcomes, models, and analysis from Data Scientists, and put them into production.
Responsibilities   Accountable for the design, development, and maintenance of scalable production machine learning pipelines and end to end AI solutions.
Utilize Big Data and Cloud based technologies to implement and scale machine learning models.
Interact with Geotab’s Big Data infrastructure on Google Big Query using Python and SQL.
Interact with other Geotab’s internal teams to implement end-to-end solutions.
Process, cleanse, and verify the integrity of data used for prediction and model building.
Select features, build, and optimize classifiers using machine learning techniques.
Use machine learning packages (e.g. Scikit-learn and Tensorflow) to develop ML models, as well as build and maintain software to manage models.
Interface with product managers, data engineers, data scientists, and software developers to gather requirements.
Make recommendations for new metrics, techniques, and strategies to improve Geotab product suite.
Support a platform providing ad‑hoc and automated access to large datasets, models, and predictions.
Manage team expectations with regards to task assignments, work arrangements, and other department expectations.
Provide encouragement to team members, including communicating team goals and identifying areas for new training or skill checks.
Qualifications   Post‑secondary Degree/Diploma specialization in Computer Science, Software/Computer Engineering, Physics, Statistics, Mathematics, or a related field.
5‑8 years experience in applied machine learning, working with large datasets to solve real‑world problems.
5‑8 years experience in deep learning frameworks, ML libraries, and computing frameworks.
Leadership experience in a team‑oriented workplace.
Demonstrated knowledge of relevant libraries and operating systems.
Familiarity with SQL and No‑SQL databases.
Strong understanding of probability theory and data modeling.
Experience in statistical analysis, quantitative analytics, forecasting/predictive analytics, multivariate testing, and optimization algorithms.
Experience in AI/ML, data pipeline building, and software engineering.
Benefits   Flex working arrangements
Home office reimbursement program
Baby bonus & parental leave top up program
Online learning and networking opportunities
Electric vehicle purchase incentive program
Competitive medical and dental benefits
Retirement savings program
The above are offered to full‑time permanent employees only

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