Senior Product Manager - Scout

{"company": " About Daloopa, Inc. Daloopa \u00a0 is transforming how investment \u00a0 professionals \u00a0 work. Our mission is to \u00a0 eliminate \u00a0 the slow, error-prone parts of fundamental research, without sacrificing accuracy or auditability.\u00a0 \u00a0 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.\u00a0 \u00a0 Today, \u00a0 Daloopa \u00a0 powers the workflows of leading investment teams \u00a0 worldwide helping \u00a0 them move faster, think deeper, and make every decision with confidence. If \u00a0 you\u2019re \u00a0 passionate about building technology that solves real-world problems and want to make an impact, join \u00a0 us \u00a0 and help redefine the future of investment research. \u00a0 Daloopa was named one of Fast Company's Most Innovative Companies in 2026 !

Why \u00a0 Daloopa? \u00a0 \u00a0 Competitive pay + performance incentives\u00a0 \u00a0 Equity in a fast-growing FinTech\u00a0 \u00a0 Hybrid work schedule (3 days in-office for NYC based positions)\u00a0 \u00a0 Career growth and mentorship opportunities\u00a0 \u00a0 A vibrant, collaborative culture\u00a0 \u00a0 Full benefits package \u00a0 ", "role": " About the Role Location: \u00a0Toronto, Canada (Remote) Compensation: \u00a0$225,000 - $245,000\u00a0CAD base salary\u00a0+\u00a0equity +\u00a0benefits\u00a0(This range is reflective of level of contribution.

Candidates who fully match what\u00a0we\u2019re\u00a0looking for in this role will be at the top of the\u00a0range.) Scout is\u00a0Daloopa's\u00a0AI-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.\u00a0That means deciding what gets built, why it gets built, and making sure it actually changes how analysts work.\u00a0This is not a coordination role.

You will have real ownership, real accountability, and the room to make calls that shape one of the company\u2019s biggest product bets. The\u00a0problem\u00a0space is challenging. Financial analysts are expert users with low tolerance for noise and\u00a0high expectations\u00a0for 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: Contribute to the Scout product roadmap: help to set the direction, sequence the bets,\u00a0and\u00a0make sure every bet ladders up to a company\u00a0objective Define analyst workflows end-to-end: understand what analysts\u00a0actually do\u00a0in modeling, comps, and research, and build against the real workflow, not the idealized one Work directly with engineering and design daily: write actionable specs,\u00a0participate\u00a0in 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\u00a0retain, and\u00a0stay close enough to the field to hear what is not working Prototype\u00a0and pressure-test ideas directly: use AI tools to build working prototypes, put them in front of customers to\u00a0validate\u00a0or kill your hypotheses, and discard weak ideas before they consume engineering time Who You Are: 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 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 Worked closely enough with engineers to understand\u00a0implementation\u00a0tradeoffs, not just outcomes Partnered with a designer through real disagreement and know the difference between a UX opinion and a UX decision Comfortable sitting with expert users, asking uncomfortable questions, and changing your roadmap because of what you heard Built for\u00a0expert users in a technical domain.

Direct exposure to financial workflows, Excel-native products or data-heavy tools is a strong plus Write clearly, speak directly, and do not need a lot of process around you to produce results Operated\u00a0in 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\u00a090 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\u00a0moved\u00a0and you can trace the movement back to specific product decisions The GTM team can demo Scout confidently, counter 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\u00a0after:\u00a0what 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. "}