---
title: 'Founding Data Scientist at Zoomlogi'
canonical: 'https://feeny.ai/job/founding-data-scientist-zoomlogi-san-francisco-5trvmect5jmv'
type: 'job'
last_seen: '2026-09-12'
---

# Founding Data Scientist at Zoomlogi

- **Company:** Zoomlogi
- **Location:** San Francisco, CA
- **Compensation:** $180k–$260k
- **Employment:** full-time
- **Work type:** hybrid
- **Posted:** 2026-06-01
- **Last confirmed live:** 2026-09-12
- **Apply:** https://jobs.ashbyhq.com/zoomlogi/f066b4ef-6342-435f-82ac-2ad82a965edd

## Job description

Nobody calls a logistics coordinator to say things went well. They call because a $100,000 shipment of clinical trial medication has been sitting in customs for three days and nobody can explain why. Because a biologic therapy arrived outside its temperature window and the treatment has to start over. Because the patient is waiting and nobody in the supply chain can give a straight answer about the shipment’s location. This is the problem we’re solving. We’re a year in, tracking over 500,000 shipments (including for Fortune 100 customers), and we’ve already cut manual ops effort in half. General Catalyst, Eclipse Ventures, and Virtue led the seed. The angels are all former or current operators who've spent careers tracking down shipments themselves, such as Head of Logistics at Bristol Myers Squibb, CMO of Cardinal Health, President of Novo Nordisk US, President of UPS Air, and CEO of Uber Freight. They know the market is as large as the problem is broken. What we’ve built works, and the most interesting problems are still ahead of us.

- Olivier, Co-founder & CEO

## The Role

Founding data scientist. The platform sees more about a shipment than anyone else in the supply chain does. Your job is to turn that into prediction: models that flag the customs hold, the temperature excursion, and the silent carrier before the issue reaches a patient. This is the Predict in our See/Predict/Act framework, and it is the part customers cannot get anywhere else. You'll own it end to end: the models, the data and feature infrastructure they run on, and the experiments that prove they work. You'll define how we measure a model, not just build one. Every person at ZoomLogi talks directly to customers, and you will too. The ops manager who tells you which exceptions actually hurt is the best feature-selection input you'll ever get.

## What You'll Build

- The risk and prediction models. The platform ingests a continuous stream from carriers, forwarders, IoT sensors, weather, and flight data, stitched into a unified picture of every active shipment. You'll refine the models that scan that picture for risk and flag issues before they cascade, across hundreds of carriers, lanes, and sensor types. The hard part is the long tail and the asymmetry: a missed signal is measured in patient outcomes, and a noisy one trains operators to ignore you. Getting precision and recall right on real shipments is the work.
- Dynamic re-routing optimization. Detecting a problem is half the job. The other half is deciding what to do about it: which alternate routing, carrier, or mode actually saves the shipment, and whether the fix is worth its cost and risk. You'll build the optimization that turns a flagged exception into the best recoverable plan, balancing transit time, temperature exposure, cost, and the hard constraints a healthcare payload carries. This is the Act in See/Predict/Act, and it is where a model stops being an alert and starts being a decision.
- The data and feature infrastructure. A model is only as good as what feeds it. You'll own the pipelines and feature layer that turn 50+ messy external sources into model-ready signal at low latency. Some sources claim a shipment is delivered while the GPS shows it still in transit. Some go silent without warning. Building features that hold up against that, and that the next model can reuse, is yours.
- The experimentation system. How do we know a model is actually working on live shipments? You'll build the offline evaluation and online experimentation that answers that: backtesting against real outcomes, measuring lift, and catching drift before customers do. You'll define the metrics the rest of the team trusts.

Who Thrives Here

- You've shipped a model to production and watched what it did there. You have a specific recent example of something you built, owned, and put in front of real decisions, and you care about what happened after it shipped. Notebook-to-nowhere work is not what this is.
- You're comfortable with ambiguity at the problem level. The interesting problems here don't arrive as labeled datasets with a defined target. You hear something from a customer, decide what's worth predicting, find or build the signal, and ship it. If you want the problem fully specified before you start, this role will frustrate you.
- You find the domain genuinely interesting. The compliance constraints, the quality and logistics tension, and the fact that your model's output moves medication to real patients should read as compelling design constraints, not obstacles.
- You're honest about what you don't know. We debate hard, change our minds, and challenge each other, always assuming everyone in the room is trying to get it right. A model that looks good for the wrong reason is something you want to catch, not defend.
- You don't think customer contact is a tax on your time. The ops people on those calls will tell you which errors actually cost something. That is signal you can't get from the data alone.

This role is probably not right for you if you prefer to go deep on one research problem and be left alone, or if you measure success by offline metrics rather than shipped impact. The surface area is wide: detection models, feature infrastructure, experimentation, and agent evaluation. If switching between modeling and infrastructure drains you, this will too.

## What We're Looking For

You have 3-9 years of applied data science or ML experience. You've put models into production, with the evaluation and monitoring that keeps them honest, not just trained them offline. You're strong in Python and the modern data and ML stack. You're comfortable owning data infrastructure: pipelines, feature engineering, and the messy work of making real-world data usable. You have real experimentation rigor and know the difference between a model that scores well and a model that works, and you can design the test that tells them apart. You understand data modeling and how your work connects to the systems around it. You're based in San Francisco or Chicago and excited about being in the office.

The Team Founded by a team of operators with deep experience in Logistics Tech & Healthcare (Ex-Uber Freight GM + Airspace CRO), joined by rockstar engineers from the likes of Abbott, Uber, Hippocratic AI, BAM (Hedge fund), and others. We're here to solve a real-world problem at scale, by making every critical shipment visible, predictable, and on time, so potentially life-saving therapies reliably reach the people who need them.

The Stack Python, React/TypeScript, Kafka, AWS. The data layer runs on a continuous stream stitched across 50+ sources. We do a lot of low-fidelity prototyping, Google Slides included. LLM infrastructure and orchestration are increasingly central to how we build.

The Interview Process We move quickly.

- Intro call: role fit, motivation, what you've shipped
- Technical screen: a realistic modeling and data problem close to the actual work
- Onsite: we'll work through a detection or prediction problem together
- References then Offer

## Compensation & Logistics

- Base salary: $180K-$260K
- Equity: Above market (4-year vesting, 1-year cliff)
- Benefits: Best-in-class medical and dental (100% self, 50% dependents)
- Location: San Francisco HQ (Mission) or Chicago (The Mart). Hybrid, 4 days/week in office. No remote application accepted

## About Zoomlogi

## Company Overview
- **One-liner**: ZoomLogi provides an AI-native operating system and control tower for healthcare and life sciences logistics, offering real-time visibility, risk prediction, and automated voice/email interventions for sensitive shipments.
- **Entity Type**: Private (Seed/Series A stage — specific funding round not disclosed publicly)
- **Headquarters**: San Francisco, California, United States
- **Founded**: 2024
- **Founders**: Not publicly disclosed

## Core Business
- **Primary industry**: Healthcare logistics / Supply chain technology
- **Target customers**: B2B — pharmaceutical companies, biotech firms, specialty pharmacies, and logistics teams managing sensitive healthcare shipments (Fortune 100 and healthcare innovators)
- **Mission statement**: "Take the guesswork out of healthcare logistics" — giving ops teams real-time visibility, risk predictions, and AI-powered workflows so they spend less time chasing status updates and more time ensuring product integrity and patient outcomes.

## Products & Services
- **AI-Native Control Tower**: A single, real-time view of all shipments across lanes, carriers, airports, and sensors, replacing multiple portals, spreadsheets, calls, and emails.
- **Multi-Channel Notification System**: Escalates exceptions and delays via email, SMS, Slack, Microsoft Teams, and AI voice/text to ensure the right contact acts fast.
- **Custom AI Workflow Builder**: No-code tool to build AI voice and email workflows for inbound and outbound operations, automating playbooks for risk management, status calls, and support.
- **Analytics & Insights**: Drill-down on all shipments with raw data by location, lane, courier, and airport to pinpoint exceptions and drive accountability.
- **System of Record**: Centralizes shipment history, messaging, documents, and proof of delivery (PODs) in one searchable home for audits.
- **Stakeholder Visibility Portal**: Branded tracking links or role-based portal access for sponsors, sites, and customers.
- **SOP & Playbook Capture**: Turns existing SOPs into no-code workflows; AI voice and email agents execute the playbook automatically.

## Market Standing
- **Valuation**: Not disclosed
- **Key Metric**: 500,000+ shipments tracked; trusted by Fortune 100 and healthcare innovators
- **Notable Investors/Partners**: Backed by leaders in healthcare and logistics (specific names not publicly listed)
- **Growth Signals**: Founded in 2024 and already tracking over 500,000 shipments; actively hiring for multiple founding roles (Head of Engineering, Founding Forward Deployed Engineer, Full-Stack Engineer, Founding Data Scientist, Founding Enterprise Account Executive); expanding team in San Francisco with a hybrid work model.

## Competitive Advantages
- **AI-native architecture**: Purpose-built for healthcare logistics, not a generic control tower or courier portal retrofit.
- **Real-time risk prediction**: Predicts issues like weather delays, customs holds, and other risks before they happen.
- **Automated interventions**: AI voice and email agents execute SOPs and playbooks automatically, reducing manual follow-up.
- **Rapid deployment**: Customers can go live in days, not months, with no long IT projects.
- **Healthcare-grade security and compliance**: Enterprise-grade security designed for sensitive pharmaceutical and biotech shipments.

## Strategic Focus
- Scale the AI-native operating system to become the standard for healthcare and life sciences logistics.
- Enable teams to handle growing shipment volume without adding headcount.
- Expand integrations with leading couriers, healthcare-grade IoT sensors, and data loggers.
- Continue building out AI-driven risk awareness and automated workflows to reduce loss rates and total logistics costs.

## Why Work Here
- **Culture**: Mission-driven environment focused on patient-first logistics — "helping deliver life-saving therapies on time, every time." The company emphasizes building "human-first tools that empower ops teams."
- **Work model**: Hybrid — employees engage in a combination of remote and on-site work (offices in San Francisco, CA, USA).
- **Engineering culture**: Opportunity to join as a founding team member in a fast-growing startup (founded 2024) with significant ownership. Roles include Head of Engineering, Founding Forward Deployed Engineer, Full-Stack Engineer, and Founding Data Scientist.
- **Notable perk**: Direct impact on healthcare logistics for life-saving therapies; opportunity to work with AI, real-time data, and automation at scale.
- **Salary**: Head of Engineering role listed at $230k–$270k/year.

## Sources
1. [ZoomLogi Official Website](https://zoomlogi.com/)
2. [ZoomLogi About Page](https://zoomlogi.com/about)
3. [ZoomLogi Careers Page](https://zoomlogi.com/careers)
4. [Built In - ZoomLogi Company Profile](https://builtin.com/company/zoomlogi)
5. [ZoomLogi Ashby Careers Portal](https://jobs.ashbyhq.com/zoomlogi)

## Other roles at Zoomlogi

- [Forward Deployed Engineer (FDE)](https://feeny.ai/job/forward-deployed-engineer-fde-zoomlogi-chicago-1xqdfa3th9mg) — Chicago, IL
- [Full-Stack Engineer](https://feeny.ai/job/full-stack-engineer-zoomlogi-chicago-v27mk35zcv3h) — Chicago, IL
- [Founding Product Designer](https://feeny.ai/job/founding-product-designer-zoomlogi-san-francisco-sjrrz34rzmxp) — San Francisco, CA
- [Forward Deployed Engineer (FDE)](https://feeny.ai/job/forward-deployed-engineer-fde-zoomlogi-san-francisco-wwwvjff8mwrk) — San Francisco, CA
- [Founding Enterprise Account Executive](https://feeny.ai/job/founding-enterprise-account-executive-zoomlogi-san-francisco-4m4y743at363) — San Francisco, CA
- [Full-Stack Engineer](https://feeny.ai/job/full-stack-engineer-zoomlogi-san-francisco-py6ndwarzynm) — San Francisco, CA
- [Founding Data Scientist](https://feeny.ai/job/founding-data-scientist-almedia-berlin-axgqcfztzkm6) — Berlin, Germany