AI Strategist, Financial Services

Responsibilities Own strategic financial-services engagements Lead discovery with banks, PE firms, hedge funds, and asset managers: what matters to the investment team, what problems they are solving, and which workflows are worth automating Define scope—what is the right thing to build—and own success metrics, pilot design, and commercial structure Drive deployment and rollout across front-office teams, with real revenue responsibility alongside Sales Be the demo and technical proof engine Build and deliver compelling, workflow-specific demonstrations: screening, comps, diligence, earnings analysis, research synthesis, portfolio monitoring Prototype workflows, skills, and connector configurations hands-on in Perplexity Computer and the API Platform to prove value in the customer's own use cases Run pilots and evaluations that hold up to scrutiny from skeptical analysts and PMs Bring domain judgment to the product Translate how deal teams, research desks, and portfolio managers actually work into product, connector, and data-partnership priorities Assess whether we have the right market data, filings, transcripts, and research integrations to serve each finance sub-vertical—and drive the roadmap where we do not Codify repeatable engagement patterns: discovery templates, finance-specific reference workflows, demo libraries, and deployment playbooks Operate as the commercial lead Own senior relationships with investment professionals and technology leaders; move engagements through qualification, scoping, proposal, pricing, legal, and close Create urgency, write crisp follow-ups, and know when to push and when to wait

Qualifications 3+ years of direct experience in investment banking, private equity, hedge funds, equity research, or institutional asset management—you have built models, written memos, and lived the workflows you will now automate 6+ years of total experience spanning finance and some combination of technical GTM, sales engineering, solutions architecture, AI product strategy, or applied AI with customer exposure Deep fluency in front-office workflows: screening, comps, diligence, earnings, research synthesis, portfolio construction and monitoring Strong intuition for AI products, agents, and workflows, with the ability to understand technical systems without needing every detail pre-digested Comfortable working shoulder-to-shoulder with Forward Deployed Engineers and technical teams: scoping builds together, pairing on prototypes, and translating customer

requirements into engineering-ready specs Demonstrated ability to turn ambiguous customer problems into scoped, executable deployment plans with clear success metrics Strong commercial instincts and comfort operating with MDs, partners, PMs, CIOs, and CTOs Excellent written and verbal communication; able to write proposals, customer memos, technical scopes, and launch narratives High agency and low ego, with the ability to run multiple complex customer workstreams in parallel and make progress before the playbook exists Nice-to-Haves Hands-on experience building with agentic systems, tool-use workflows, MCP, or LLM APIs Working knowledge of institutional data platforms: FactSet, Bloomberg, S&P Capital IQ, PitchBook, Morningstar, or similar Experience selling or deploying technology into regulated financial institutions, including security and compliance review Founder or early-startup operating experience Strong network across buy-side and sell-side institutions If you're excited about this role, we encourage you to apply even if your experience doesn't match every qualification listed above.

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