Sign up

By role

Find a data and AI co-founder for your startup

A data and AI co-founder differs from a generalist technical co-founder in their field: data collection and quality, the choice between simple rules, statistical models and machine learning, and the evaluation and monitoring of results over time. These skills are at the heart of products that predict, recommend or automate.

Publish my project for freeBrowse talent

In short

To find a data and AI co-founder, start from the data your product depends on (where it comes from, its quality, your right to use it), publish your project for free on EasyCofounder describing the problem to solve rather than the technique, then give candidates a small analysis on a real sample before you commit.

Profiles differ depending on the need: the data scientist who explores and models, the machine learning engineer who puts models into production reliably, and the data engineer who builds data pipelines. An early-stage project often looks for one person able to cover all three at small scale.

On EasyCofounder, describe the problem, the data available and the constraints of your market, state the role and equity on offer, then talk to data and AI talent whose skills and availability fit. The AI matching highlights the profiles most complementary to yours.

Many data and AI startups today build on models provided by third parties rather than training their own from scratch. That changes the profile you need: someone able to evaluate several models, design prompts and retrieval systems, control costs and handle failures gracefully, more than a researcher focused on new architectures. Make it clear in your project which approach you expect.

Data access is also a negotiation. If your product depends on customer data, the co-founder must be able to explain to buyers how it is stored, protected and used, and to work with lawyers on contracts that clarify who owns what.

What a data and AI co-founder brings

  • Data before the model

    A good data co-founder first checks access, quality and usage rights for the data, because a high-performing model never makes up for incomplete or unusable data.

  • Regulation to build in

    In Europe, GDPR applies as soon as personal data is processed, and the EU AI Act sets obligations depending on the use case. Check this early with a specialist lawyer, along with the rules in your other markets.

  • From demo to product

    Moving from a convincing prototype to a reliable service requires monitoring, testing and control of compute costs, experience you should ask candidates about explicitly.

  • Evaluation as a core skill

    Knowing how to build a test set and measure quality over time is what turns an impressive demo into a product customers can trust.

  • Cost control

    Compute and model usage costs can grow quickly with users. A good data co-founder designs the product so that margins remain healthy at scale.

  • Data in customer contracts

    Enterprise buyers will ask how their data is used. Clear answers and contracts help close deals and protect the company.

Find your co-founder in 4 steps

  1. 1

    Create your free profile

    Describe your skills, your availability and what you bring to a founding team.

  2. 2

    Publish your project

    Say which role you are looking for (CTO, CEO, CMO…), the commitment expected and the equity on offer.

  3. 3

    Let the matching work

    Filter talent by skill and location, or get suggestions of compatible profiles from the AI matching.

  4. 4

    Talk, then meet

    Chat through the built-in messaging, test your collaboration on a first project and put your agreement in writing in a shareholders’ agreement.

Frequently asked questions

Data scientist or machine learning engineer: who should I look for?

If your challenge is proving that a model can solve the problem, a data scientist is often the priority. If you need to serve predictions reliably to many users, production experience becomes essential.

Does a data co-founder replace a CTO?

Not always, because a complete product also needs an application, infrastructure and security. Some profiles cover both at the start, others prefer to focus on data alongside a developer.

How can I assess a data co-founder if I am not one myself?

Ask them to explain simply how they would measure the model’s success and which errors would cost your customers most, then have their work on a sample reviewed by a second person from the field.

Where can I find a data and AI co-founder?

Publish your project for free on EasyCofounder describing the problem and the data available, then filter talent by data and AI skills and availability to propose a first conversation.

Should my AI startup train its own models?

Not necessarily. Many products combine existing models with proprietary data and workflows. Training your own models makes sense when it creates a real advantage that others cannot easily copy.

How can I check a data co-founder’s practical experience?

Ask about systems they have put into production, how they monitored them and what went wrong. Public code, technical writing or talks can complement the conversation.

Guides to partner well

Find a co-founder in another city

Find a co-founder by sector

Find a co-founder by role