Manager, Data Science & Machine Learning
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Key skills for this role
About the Role
Lightspeed is seeking a hands-on Manager, Data Science & Machine Learning to lead a team of data scientists in delivering production-ready ML solutions. The role involves managing the full model lifecycle, defining best practices, and collaborating with MLOps and stakeholders.
Key Skills for This Role
Responsibilities
- Lead the full lifecycle of Data Science & Machine Learning models from experimentation to production deployment
- Own day to day management of the team, ensuring prioritization, unblocking, and delivery standards
- Define, document, and champion data science best practices covering modeling standards, code quality, and experimentation frameworks
- Serve as subject matter authority and internal resource for other data science teams
- Collaborate with MLOps team on production release and ongoing maintenance of models
- Set clear expectations and performance goals for direct reports, conduct regular 1:1s and performance calibrations
- Identify growth opportunities and build individualized development plans for team members
- Participate in project planning and technical brainstorming with business stakeholders
- Represent the team's work in leadership forums, steering committees, and quarterly business reviews
Requirements
- 3+ years of hands on data science experience with production model deployment
- 4+ years of experience directly managing a team of data scientists
- Proficiency in Python
- Familiarity with MLOps tooling (e.g., MLflow, Vertex AI, Databricks)
- Strong understanding of supervised/unsupervised ML and model evaluation
- Comfort with cloud based ML platforms (AWS, GCP, or Azure)
- Experience with ML engineering practices (model serving, monitoring, drift detection, retraining pipelines, feature stores)
- Strong communication skills to translate technical work for executive audiences
Full Job Posting
Role Overview
- We're looking for a Manager, Data Science & Machine Learning to join our Data team in Canada.
- This is a hands on leader responsible for guiding a high performing team of data scientists to deliver impactful, production ready solutions.
- The role drives Data Science & Machine Learning model delivery from experimentation through production and owns the Data Science Enablement roadmap.
What you'll be doing
- Lead, oversee and own the full lifecycle of Data Science & Machine Learning models from experimentation to production deployment.
- Own day to day management of the team by ensuring the right work is prioritized, the team is unblocked, and delivery standards are consistently met.
- Define, document, and champion data science best practices covering modeling standards, code quality, experimentation frameworks, and documentation.
- Serve as a subject matter authority and internal resource for other data science teams.
- Collaborate with Data Science leads to align on standards and create a cohesive data science community of practice.
- Collaborate with the MLOps team on production release and ongoing maintenance of models.
- Set clear expectations and individual performance goals for all direct reports.
- Conduct regular 1:1s, provide timely and actionable feedback, and lead performance calibrations.
- Identify growth opportunities, sponsor stretch assignments, and build individualized development plans.
- Participate in project planning and technical brainstorming sessions with business stakeholders.
- Proactively manage expectations, surface risks early, and influence across cross functional teams.
- Represent the team's work in leadership forums, steering committees, and quarterly business reviews.
What you need to bring
- 3+ years of hands on data science experience with direct personal experience deploying models to production.
- Demonstrated experience with ML engineering practices including model serving, monitoring, drift detection, retraining pipelines, and/or feature stores.
- Familiarity with modern MLOps tooling (e.g. MLflow, Vertex AI, Databricks).
- 4+ years of experience directly managing a team of data scientists, including hiring, performance management, and career development.
- Proficiency in Python; comfortable reading and reviewing code, models, and pipeline logic.
- Strong understanding of supervised/unsupervised ML, model evaluation, and common failure modes in production.
- MLOps fluency to collaborate with Senior ML engineers in defining standards and reviewing infrastructure decisions.
- Comfort with cloud based ML platforms (AWS, GCP, or Azure) and data warehousing environments.
- Strategic thinking, ability to zoom out to prioritize for impact and zoom in to help unblock.
- Strong communication skills to translate complex technical work for executive audiences.
- Structured thinking to rapidly assess new project ideas across value, feasibility, risk, and strategic fit.
- Ability to proactively identify dependencies, risks, and blockers before they become escalations.
You'll enjoy
- A flexible work environment that empowers you to do your best work.
- A culture that celebrates performance.
- The chance to make an impact in a team that's big enough for career growth, but lean enough to make your voice heard.
- Career defining opportunities.
- Flexible paid time off and remote work policies.
- Equity options.
- Contributions to your pension plan.
- Training opportunities to grow your skills and career.
- Health and wellness credit.
- Time off to volunteer and give back to your community.
- Enhanced parental leave to support growing families.
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