✨Lead Product Manager - Aida (AI Dietitian for Groceries)

Company: Aspect HealthProduct: Aida – an AI dietitian that turns health goals into shoppable grocery cartsStage: Fully Funded: Raised $10M seed from Lux, Valor and other tier one investors. Pre-PMF.Location: Remote / flexibleReporting to: CPO & Founder (Elad Katz, ex Nike, Noom, Squarespace)Type: Full-time 🧠 Mission & why this role exists Aida helps people shop for groceries the way a great dietitian would: grounded in science, personalized to their health, and actually practical.

We already have:

• A working product (MVP)
• strong nutrition & clinical science,
• solid AI / LLM capabilities,
• strong Eng and Analytics teams,
• and a world-class marketing team. What we need now is someone to own the product end-to-end:

From intake → recommendations → cart → order → weekly retention loop.

You’ll be the Lead PM for the Aida app, reporting to Elad (CPO), and teaming up with our Head of Engineering, science team and AI team to move us from “MVP” to “product people use every week.”

Your job is to find and scale the loops that work, not just keep a backlog full.

🎯 What you’ll do You’ll own both what we build and how we learn:

• Own the full user journey Define and iterate the Aida experience from first visit and intake, through recommendations and cart creation, all the way to order and ongoing check-ins / weekly loops.

• Drive PMF through experiments (build, measure, learn) Turn strategy and hypotheses into many small, fast experiments (intake variants, meal plans vs full-cart flows, discovery views, etc.) with clear, measurable and actionable success criteria. Prioritize learning over shipping “big features.”

• Design and test “closing the loop” models Explore and validate different ways to get from profile → groceries, such as weekly meal plans, full-cart auto-builds, product discovery flows, or scanner/in-store helpers. Use both qual and quant signals to decide what to double down on.

• Own the food catalog & badging Define how our food/product database is structured: schema, tags, and health-related badges. Work with dietitians to translate clinical and nutrition rules into machine-readable constraints and scores that our systems can actually use.

• Shape our recommendation systems Own the product side of recommendations (alternatives, substitutions, ranking). Collaborate with Science & AI to define what “good” looks like, aim for high expert + user agreement (e.g. ~80%+ quality), and define success metrics and feedback loops to improve over time.

• Run scrappy validation, not just builds Use Wizard-of-Oz, concierge flows, manual ops, and prototypes to validate ideas before asking engineering for a full implementation.

• Turn user research into product decisions Continuously talk to users (US-based, English speaking), run interviews and UX tests, and synthesise insights into concrete product opportunities and changes.

• Align a fast engineering team Work directly with the Head of Engineering to prioritize, slice scope, and sequence work so that our very fast eng team is always building things that can be measured for value. Don’t feed the beast, move the business metrics forward!

• Make data useful, not fancy Work with our crack analytics to define simple, early metrics for each experiment (e.g. intake → first-cart conversion, rec usage, re-orders, short-term retention) and use data to decide what to improve, pivot, or kill.

✅ What you’re great at- The ideal candidate would align with:

• 0→1 / pre-PMF ownership You’ve owned a consumer product or major area end-to-end in an early-stage startup (strategy → experiments → delivery → iteration) and are comfortable with ambiguity and change.

• Build–Measure–Learn mindset You naturally translate ideas into small tests with clear success/failure criteria and are comfortable killing things quickly when the data (or users) tell you to.

• Catalog / schema / taxonomy thinking You’ve worked with a substantial product or content catalog (ideally grocery / food / CPG) and have defined schemas, tags, and rules that power user-facing filters, badges, or scoring.

• Experience with AI recommendation systems and LLMs You’ve partnered with AI / data teams on recommendation or ranking systems (rule-based, ML, or hybrids) and care deeply about measuring and improving quality, not just shipping “AI features.”

• Translating experts into product behavior You’ve worked with clinicians, scientists, or other domain experts and can turn complex guidance into concrete flows, rules, thresholds, and UX that users actually understand.

• Strong collaboration with Engineering You’re used to working directly with a Head of Eng / CTO to allocate resources, negotiate scope vs speed, and keep a lean team focused on the highest-impact experiments.

• User discovery & communication You’re very comfortable running interviews and tests in English with a US user base and turning what you learn into sharp product decisions.

• Data-informed, not data-paralyzed You have worked with analytics partners, defined simple metrics and win/kill thresholds, and made decisions quickly—even when the data is messy and incomplete.

Bringing clarity to ambiguity

You’re good at taking messy, changing inputs and translating them into a clear, prioritised plan with crisp specs and unambiguous next steps for design and engineering.

Our Company Aspect is filled with great people, we value our culture and we only hire the best humans.

image.png

Here’s a picture of part of the team from our company offsite in Georgia!

We work hard and play hard, want to be part of the fun, apply now!