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Founding Data Scientist Jobs (NOW HIRING)

ZoomLogi

Founding Data Scientist

Chicago, IL · On-site

$180K - $240K/yr

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 ...

Nash, Inc.

Senior Data Scientist

San Francisco, CA · On-site +1

Senior Data Scientist About Nash Nash is the autonomic logistics platform. We unify decisioning and ... Prior experience at an early-stage company or in a founding data role. Why this role matters The ...

Percepta

Founding Data Engineer

New York, NY · On-site

$150K - $400K/yr

About The Role We're hiring one of the founding members of Percepta's data team - a role that lives across the full spectrum from data engineering to data science to ML engineering. You won't be ...

New

Outtake

Founding Applied Data Scientist

New York, NY · On-site

About the role We're looking for a Founding Applied Data Scientist to define how Outtake understands, measures, and improves the performance of our product, business, and AI systems. At a high level ...

Autodesk

$159K - $285K/yr

This is a founding data science role for our agentic product platform - a next-generation framework that combines domain-specific AI agents into a unified harness enabling complex, multi-step ...

Publicis Groupe Holdings B.V

Data Scientist

Chicago, IL · On-site

We are an agency still grounded in our founding principle that people are at center of all we do ... Overview The Data Scientist at Starcom will play a hands-on role in creating, testing and scaling ...

Publicis Groupe Holdings B.V

Data Scientist

Chicago, IL · On-site

We are an agency still grounded in our founding principle that people are at center of all we do ... Overview The Data Scientist at Starcom will play a hands-on role in creating, testing and scaling ...

GRVTY

Data Scientist

Mclean, VA · On-site

... program since our founding in 2013. We've grown on this effort by providing the customer with ... The Data Scientist will be working in a fast-paced, dynamic, agile software development environment.

JTEC Consulting

Data Scientist

Mclean, VA · Hybrid

$160K - $220K/yr

Data Scientist McLean, VA 22102 JTEC Consulting LLC focuses on successfully delivering solutions to ... Our founding members have decades of experience delivering a wide range of solutions to Air Force ...

JTEC Consulting LLC

Data Scientist

Mclean, VA · On-site

$160K - $220K/yr

Data Scientist McLean, VA 22102 JTEC Consulting LLC focuses on successfully delivering solutions to ... Our founding members have decades of experience delivering a wide range of solutions to Air Force ...

Parafin

Data Scientist

San Francisco, CA · Remote

The Data Science TeamOur Impact Data is core to Parafin's mission to grow small businesses. Our ... Since our founding, we have grown the team to include perspectives from graduate studies in physics ...

Parafin, Inc

Data Scientist

San Francisco, CA · On-site

The Data Science Team Our Impact Data is core to Parafin's mission to grow small businesses. Our ... Since our founding, we have grown the team to include perspectives from graduate studies in physics ...

GRVTY

Data Scientist

Mclean, VA · On-site

... program since our founding in 2013. We've grown on this effort by providing the customer with ... GRVTY is seeking a Data Scientist with a TS/SCI + Poly clearance (acceptable to this customer) to ...

GRVTY

Data Scientist

Mclean, VA · On-site

... program since our founding in 2013. We've grown on this effort by providing the customer with ... GRVTY is seeking a Data Scientist with a TS/SCI + Poly clearance (acceptable to this customer) to ...

GRVTY

Data Scientist

Mclean, VA

... program since our founding in 2013. We've grown on this effort by providing the customer with ... GRVTY is seeking a Data Scientist with a TS/SCI + Poly clearance (acceptable to this customer) to ...

Alpha Omega Integration

Data Scientist

Columbia, MD · On-site

$117K - $137K/yr

Data Scientist Clearance Required: US Citizen, Public Trust Clearance Work Location: Hybrid, Camp ... Since our founding in 2016, we have grown to over 700 employees nationwide consisting of former ...

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How much do founding data scientist jobs pay per year?

As of Aug 6, 2026, the average yearly pay for founding data scientist in the United States is $122,738.00, according to ZipRecruiter salary data. Most workers in this role earn between $98,500.00 and $136,000.00 per year, depending on experience, location, and employer.

What are the key skills and qualifications needed to thrive as a founding data scientist?

To thrive as a Founding Data Scientist, you need expert knowledge in statistics, machine learning, and data analysis, often supported by an advanced degree in a quantitative field. Familiarity with tools such as Python, R, SQL, cloud platforms (e.g., AWS, GCP), and experience with data pipeline and model deployment frameworks is typically required. Strong problem-solving abilities, entrepreneurial mindset, and the ability to communicate complex ideas clearly set exceptional candidates apart. These skills are crucial for building reliable data products from scratch, influencing company direction, and driving data-driven decision-making in an early-stage environment.

What is a founding data scientist?

Founding Data Scientists are data professionals who join a company at its earliest stages, often as one of the first technical hires. They are responsible for setting up the company's data infrastructure, developing initial machine learning models, and establishing best practices for data analysis. Their role is highly strategic, often collaborating closely with founders to influence product direction and data-driven decision-making. In addition to technical expertise, Founding Data Scientists need to be adaptable, entrepreneurial, and comfortable working in fast-paced, uncertain environments.

What are some unique challenges a founding data scientist faces in an early-stage startup environment?

As a Founding Data Scientist in an early-stage startup, you often wear multiple hats, balancing hands-on model development with strategic planning and infrastructure setup. You'll likely be responsible for establishing data processes, selecting tech stacks, and setting up data pipelines from scratch, often with limited resources. Close collaboration with engineering, product, and leadership teams is essential, as your insights will directly influence business strategy. This role demands adaptability, strong communication skills, and a proactive mindset, as priorities can shift rapidly in a startup setting.
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Founding Data Scientist jobs near you
Infographic showing various Founding Data Scientist job openings in the United States as of July 2026, with employment types broken down into 1% As Needed, 84% Full Time, 11% Part Time, 1% Temporary, and 3% Contract. Highlights an 87% Physical, 3% Hybrid, and 10% Remote job distribution, with an average salary of $122,738 per year, or $59 per hour.

Founding Data Scientist

ZoomLogi

Chicago, IL • On-site

$180K - $240K/yr

Full-time

Medical, Dental

Re-posted 5 days ago

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-$240K
  • 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.

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