Manager, Strategic Finance

Lyft Lyft · Consumer · Toronto, ON · FP&A

Manager, Strategic Finance role at Lyft, focusing on financial analysis for strategic decision-making, long-range and annual planning, competitive intelligence, and ad-hoc analysis. Requires strong financial modeling and business acumen.

What you'd actually do

  1. Build and own a sophisticated, multi-year, full company model that quantifies Lyft's financial potential, and serve as the connective tissue between corporate strategy and LOB P&L owners.
  2. Translate the Long-Range Plan into Annual Plan targets by line of business, construct a defensible long-range plan to annual plan bridge, and assist in creating durable line of business accountability against external financial commitments.
  3. Produce strategic intelligence briefs following competitor earnings or other announcements.
  4. Lead financial analysis for ad-hoc strategic projects and executive requests, often under tight timelines and with incomplete information.

Skills

Required

  • 5+ years in investment banking, management consulting, strategic finance, or corporate finance
  • financial modeling excellence
  • strong business acumen
  • ability to connect financial metrics to underlying business drivers and competitive dynamics
  • experience building financial models from scratch, P&Ls, cash flow projections, scenario analyses, sensitivity tables
  • comfort operating in ambiguity
  • structuring loosely-defined problems
  • making defensible assumptions
  • driving to decisions with imperfect information
  • excellent communication skills
  • ability to tailor messaging for different audiences
  • strong attention to detail
  • ability to deliver accurate, high-quality work
  • managing competing priorities in a fast-paced environment
  • Bachelor's degree in Finance, Accounting, Economics, or related quantitative field

Nice to have

  • natural curiosity
  • continuous improvement mindset

What the JD emphasized

  • financial modeling excellence
  • building financial models from scratch
  • operating in ambiguity
  • structuring loosely-defined problems
  • driving to decisions with imperfect information