Head of Products - Remote

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.

Competitive pay + performance incentives

Equity in a fast-growing FinTech

Career growth and mentorship opportunities

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

Compensation: $205k - $225k annualized + equity + benefits (This range is reflective of level of contribution. 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. 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.

Evaluate AI output quality: build evaluation frameworks, define accuracy and confidence standards, and hold the bar on what ships.

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.

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.

Direct exposure to financial workflows, Excel-native products or data-heavy tools is a strong plus.

You have operated in a startup or early-stage environment and know what it means to work without a support structure.

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.

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.

Show us AI products you have shipped and what happened after launch. 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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