About Daloopa, Inc.

Daloopa is transforming how investment professionals work. Our mission is to eliminate the slow, error-prone parts of fundamental research—without sacrificing accuracy or auditability.

Founded by a former equity research analyst and top engineers, we built an AI-powered platform that converts complex financial filings, transcripts, and KPIs into clean, hyperlinked, customizable data. This means analysts spend less time on manual tasks and more time generating insights that drive performance.

Today, Daloopa powers the workflows of leading investment teams worldwide—helping them move faster, think deeper, and make every decision with confidence. If you’re passionate about building technology that solves real-world problems and want to make an impact, join us and help redefine the future of investment research.

Daloopa was named one of Fast Company's Most Innovative Companies in 2026!

Why Daloopa?

Competitive pay + performance incentives
Equity in a fast-growing FinTech
Hybrid work schedule (3 days in-office)
Career growth and mentorship opportunities
A vibrant, collaborative culture
Full benefits package

About the role

Location: New York City (Hybrid office/remote schedule)

Compensation: $205k - $225k annualized + equity + benefits (This range is reflective of level of contribution. Candidates who fully match what we’re looking for in this role will be at the top of the range.)

About the Role

Scout is Daloopa's AI-native product for the analyst workflow: not a prompt tool, not a chatbot, but an agent that understands what analysts are doing and works alongside them. As the PM for Scout, you own the product from strategy through execution. That means deciding what gets built, why it gets built, and making sure it actually changes how analysts work. This is not a coordination role. You will have real ownership, real accountability, and the room to make calls that shape one of the company’s biggest product bets.

The problem space is challenging. Financial analysts are expert users with low tolerance for noise and high expectations for accuracy. Building AI products in that context requires understanding both the workflow and the technology well enough to make honest tradeoffs. You need to know when AI is the right answer and when it is not. If you have shipped AI products that changed user behavior, stayed close to the technical work, and are ready to do that again at a company that is still early enough for your decisions to matter, this is your seat.

What You’ll Do

Own the Scout product roadmap: set the direction, sequence the bets, and sure every bet ladders up to a company objective.
Define analyst workflows end-to-end: understand what analysts actually do in modeling, comps, and research, and build against the real workflow, not the idealized one.
Work directly with engineering and design daily: write actionable specs, participate in technical design discussions, and make real-time decisions when the work surfaces new constraints.
Drive repeat usage by defining what healthy engagement looks like for a workflow shaped by the quarterly earnings cycle, instrument it properly and turn first uses into the repeat behavior that compounds into durable retention.
Evaluate AI output quality: build evaluation frameworks, define accuracy and confidence standards, and hold the bar on what ships.
Own analyst discovery: get in the room with users, watch the real workflow, and come back with a decision and the evidence behind it.
Enable the GTM team: give sales and CS the product context they need to sell and retain, and stay close enough to the field to hear what is not working.
Prototype and pressure-test ideas directly: use AI tools to build working prototypes, put them in front of customers to validate or kill your hypotheses, and discard weak ideas before they consume engineering time.

Who You Are

You have 5+ years of product management experience and have owned an AI product or a complex workflow product end-to-end, not as a contributor, as the owner.
You can walk through the hardest problem you hit shipping an LLM-powered product and what you changed, whether the fix was in evals, tool design, retrieval, or prompting.
You have worked closely enough with engineers to understand implementation tradeoffs, not just outcomes.
You have partnered with a designer through real disagreement and know the difference between a UX opinion and a UX decision.
You are comfortable sitting with expert users, asking uncomfortable questions, and changing your roadmap because of what you heard.
You have built for expert users in a technical domain. Direct exposure to financial workflows, Excel-native products or data-heavy tools is a strong plus
You write clearly, speak directly, and do not need a lot of process around you to produce results.
You have operated in a startup or early-stage environment and know what it means to work without a support structure.
You already use AI tools in your daily work and can show it, not just say it.

Success Looks Like

Within 90 days, you know the Scout product deeply: every user flow, every known failure mode, and every open bet on the roadmap.
Within a few months, you have shipped at least one meaningful workflow improvement that changed how analysts use the product, with data to show it.
Within 12 months, Scout's retention and engagement metrics have moved and you can trace the movement back to specific product decisions.
The GTM team can demo Scout confidently, object to competitive claims, and explain what makes it different, because you gave them the context to do it.
Engineering and design treat you as the person who makes the hard call, not the person who waits for consensus.

Apply

Show us AI products you have shipped and what happened after launch. We want to see the before and after: what changed, what you measured, and what you would do differently. If you have built evaluation frameworks, run user research in technical domains, or made a hard call on an AI product that you can walk through in detail, we want to hear about it.

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