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Senior Data Scientist, Algorithms

3 hours ago

Toronto, Ontario, Canada Lyft Full time

At Lyft, our purpose is to serve and connect. We aim to achieve this by cultivating a work environment where all team members belong and have the opportunity to thrive.

Data Science is at the heart of Lyft's products and decision-making. Data Scientists at Lyft operate in dynamic environments, moving quickly to build the world's best transportation solutions. We tackle a wide range of challenges—from shaping long-term business strategy with data, to making critical short-term decisions, to developing algorithms and models that power both internal systems and customer-facing products.

Lyft Business builds products that help organizations move the people who matter most—employees, customers, patients, and guests—easily and efficiently. Our offerings include Business Travel, Lyft Pass, and Concierge (for healthcare and non-healthcare rides), enabling companies to manage transportation at scale through APIs, integrations (e.g., Concur, Expensify), and dedicated tools. These platforms power high-impact B2B use cases across corporate travel, healthcare access, customer experience, and community programs.

We are seeking a Data Scientist to lead initiatives across the entire Lyft Business product suite. In this role, you will shape the vision, define the roadmap, and drive execution for data science projects that accelerate growth, improve operational efficiency, and deliver measurable value to our partners. You'll collaborate closely with Product, Engineering, Design and Go-to-Market teams to build models, experimentation frameworks, and advanced analytics that inform strategy and power product innovation.

This is a high-visibility, high-impact role with direct influence on Lyft's enterprise offerings. The ideal candidate will bring deep expertise in algorithm development, machine learning, causal inference, experimentation; strong business acumen in B2B contexts; and a proven track record of leading teams in fast-paced, cross-functional environments.

Responsibilities
  • Lead multiple Machine Learning and AI initiatives across Lyft Business (Business Travel, Lyft Pass, Concierge), operating in open-ended and ambiguous spaces with multi-team impact.
  • Design, develop, and deploy advanced ML models, optimization algorithms, and ranking/decision systems that power core product and platform capabilities.
  • Own the end-to-end lifecycle of complex modeling solutions — problem formulation, data exploration, model development, evaluation, deployment, and ongoing iteration.
  • Partner closely with Engineering to build scalable, production-grade systems, including online inference services, batch pipelines, feature stores, monitoring, and model governance.
  • Define model evaluation strategies, experimentation plans, and offline/online validation methods, ensuring algorithms are robust, reliable, fair, and aligned with business outcomes.
  • Improve model performance across latency, accuracy, stability, cost, and system reliability, employing advanced tuning, optimization, and scientific rigor.
  • Build and maintain high-quality codebases for models, training pipelines, diagnostics, and simulation frameworks; enforce best practices around reproducibility and documentation.
  • Drive algorithmic innovation, introducing new techniques in ML, optimization, reinforcement learning, causal inference, or graph-based methods that unlock new product capabilities.
  • Collaborate across Product, Engineering, Ops, and Science to translate ambiguous business problems into algorithmic solutions with measurable success criteria.
  • Mentor junior and mid-level applied scientists and data scientists, providing technical guidance, code reviews, modeling critiques, and helping raise the scientific bar.
  • Contribute to Lyft's broader Science community, particularly around ML tooling, modeling standards, experimentation practices, and reusable algorithmic frameworks.
Experience
  • Master's or PhD in Machine Learning, Computer Science, Optimization, Statistics, Engineering, or a related quantitative discipline; or equivalent strong applied experience.
  • 5+ years of industry experience developing and deploying machine learning models, algorithms, or optimization systems in production environments.
  • Demonstrated ability to independently own multi-project, multi-component algorithmic scopes, especially in ambiguous or highly technical problem domains.
  • Deep expertise in: Supervised and unsupervised learning, Optimization, probabilistic modeling, or time-series modeling, Ranking systems, decisioning, or reinforcement learning, Feature engineering, pipeline architecture, and ML deployment
  • Strong proficiency in Python, ML frameworks (PyTorch, TensorFlow, JAX, scikit-learn), and distributed data processing (Spark, Snowflake, Databricks).
  • Hands-on experience building production-grade ML systems, including real-time inference services, batch pipelines, and monitoring/alerting frameworks.
  • Demonstrated ability to define experiment design, offline metrics, online metrics, and success criteria for model evaluation.
  • Strong communication and influence skills — capable of explaining technical tradeoffs, model behaviors, and system constraints to both technical and non-technical partners.
  • Experience mentoring others, raising modeling standards, and contributing to internal ML best practices.
  • Proven track record of delivering high-quality, well-integrated algorithmic solutions that drive measurable product and business outcomes.
Benefits:
  • Extended health and dental coverage options, along with life insurance and disability benefits
  • Mental health benefits
  • Family building benefits
  • Child care and pet benefits
  • Access to a Lyft funded Health Care Savings Account
  • RRSP plan to help save for your future
  • In addition to provincial observed holidays, salaried team members are covered under Lyft's flexible paid time off policy. The policy allows team members to take off as much time as they need (with manager approval). Hourly team members get 15 days paid time off, with an additional day for each year of service
  • Lyft is proud to support new parents with 18 weeks of paid time off, designed as a top-up plan to complement provincial programs. Biological, adoptive, and foster parents are all eligible.
  • Subsidized commuter benefits

Lyft is committed to creating an inclusive workforce that fosters belonging. Lyft believes that every person has a right to equal employment opportunities without discrimination because of race, ancestry, place of origin, colour, ethnic origin, citizenship, creed, sex, sexual orientation, gender identity, gender expression, age, marital status, family status, disability, pardoned record of offences, or any other basis protected by applicable law or by Company policy. Lyft also strives for a healthy and safe workplace and strictly prohibits harassment of any kind. Accommodation for persons with disabilities will be provided upon request in accordance with applicable law during the application and hiring process. Please contact your recruiter if you wish to make such a request.

Lyft highly values having employees working in-office to foster a collaborative work environment and company culture. This role will be in-office on a hybrid schedule — Team Members will be expected to work in the office at least 3 days per week, including on Mondays, Wednesdays, and Thursdays. Lyft considers working in the office at least 3 days per week to be an essential function of this hybrid role. Your recruiter can share more information about the various in-office perks Lyft offers. Additionally, hybrid roles have the flexibility to work from anywhere for up to 4 weeks per year. #Hybrid

The expected base pay range for this position in the Toronto area is CAD $136,000 - CAD $170,000. Salary ranges are dependent on a variety of factors, including qualifications, experience and geographic location. Range is not inclusive of potential equity offering, bonus or benefits. Your recruiter can share more information about the salary range specific to your working location and other factors during the hiring process.