Real-Time Supply Optimization Scientist

Lyft; Toronto; Hybrid; CAD 108,000 - 135,000; Full time

Benefits offered by this job

Extended health and dental coverage

Mental health benefits

Family building benefits

RRSP plan with company match

Paid time off

Hybrid work model

Lyft ride credits

Job summary

Lyft is seeking a Data Scientist specialized in Algorithms in Toronto to partner with product, engineering, and operations to scale incentive systems and influence driver earnings. Strong background in optimisation, causal inference, or ML is valued, with hands-on data work and Python proficiency.

The role fits a fast-paced, entrepreneurial team and hybrid in-office schedule. The team focuses on pay mechanisms, real-time and long-horizon incentives, and marketplace efficiency through rigorous

Qualifications

• Advanced degree (MS or PhD) in a quantitative field such as OR, applied math, CS, statistics or engineering.
• 2+ years of hands-on experience in optimisation, causal inference, or machine learning.
• End-to-end data work including querying, aggregation, analysis, and visualization.
• Proficiency with Python and strong collaboration skills.
• Experience designing, running, and analyzing A/B tests.

Responsibilities

• Collaborate with engineering and product teams to design, implement, and iterate on features for driver incentives.
• Develop and deploy optimisation models and systems for budget allocation and incentive targeting.
• Write production model code and work with software engineers to put algorithms in production.
• Perform exploratory data analysis to understand the marketplace and users.
• Communicate findings and support launch decisions with technical and non-technical stakeholders.
• Apply robust experimentation and causal inference to measure impact of new features.

Skills

Python

Data analysis

A/B testing

Collaboration

Machine learning

Causal inference

Optimization

Education

MS or PhD in quantitative field

Tools

SQL

Job description

Lyft is seeking a Data Scientist specialized in Algorithms in Toronto to partner with product, engineering, and operations to scale incentive systems and influence driver earnings. Strong background in optimisation, causal inference, or ML is valued, with hands-on data work and Python proficiency.

The role fits a fast-paced, entrepreneurial team and hybrid in-office schedule. The team focuses on pay mechanisms, real-time and long-horizon incentives, and marketplace efficiency through rigorous