About this role
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.
The Rider Recommendations team is an Applied AI and Machine Learning team tasked with personalizing all realtime decisions happening inside the Rider Apps, including search, ride modes, content selection and ranking, message generation and others. Since last year, this team has been one of the major contributors to financial outcomes at Lyft and, working closely with the Rider App team, they are in the process of evolving the Rider application to make more room for personalization and realtime decision making. The team operates as the orchestration layer behind everything that the rider sees, being the final decision maker on what to show.
You will work closely with machine learning, AI engineers and data scientists to define the right objectives for the team’s models, interfacing with the rest of the company to understand the Rider App’s stakeholder teams needs and Lyft’s overall goals. In order to do this, you will have to identify the outcomes worth pursuing and translate them into a single objective to be used end to end by the applications. You’ll have to consider short term and long term tradeoffs, other than objectives of partner teams.
Responsibilities:
• Define, own and be responsible for the roadmap of applied ML and AI products in every screen of the Rider Application, through the Rider conversion funnel
• Define, track and periodically report on metrics and KPIs of the team, being directly responsible for financial outcomes that the team drives
• Define the objective function to be maximized / minimized throughout the rider app, taking into account short term, long term and stakeholder teams’ objectives (Loyalty, Partnership, Ads, Business, AV, Safety, Luxury etc)
• Work with data science to define, measure and predict long term value of Riders
• Identify how to cold start new launches of region, modes and features in order to effectively drive adoption while still maintaining algorithm performance
• Work in a cross functional team building a unified Rider profile vision by identifying, collecting and organizing information about Riders to be used for ML and AI training
• Write detailed product requirements, strategy and planning documents aimed at both technical and non technical audiences to drive alignment on product development, set expectations on delivery of milestones and create predictable outcomes for workstreams
• Design, run and analyze experiments and ongoing evaluation systems to monitor model performance and evaluate improvements
• Present to non technical leadership on the progress of the team monthly, showcasing progress and highlighting roadblocks
Experience:
• Experience driving technically complex machine learning (ML) products end to end, from inception, to launch with significant user adoption, to sunset. Ideally the applications are