Data Scientist (Unibaio)

Unibaio

We enhance crop protection efficiency through biodegradable chitosan microparticles, reducing chemical use while maintaining yields.

Data Scientist (Chemistry-focused)

At Unibaio, we are building a biodegradable nano-delivery platform that enhances how agrochemicals interact with plants — allowing farmers to reduce chemical use while maintaining or improving yields (Yes! less chemicals on our food!).
Now, we’re taking a step further.
We’re exploring how AI can accelerate formulation discovery, helping us identify which agrochemicals are most likely to perform well with our technology — before running costly and time-consuming field trials.
To do this, we’ll be competing in the final round of CodeLaunch, where we’ll build a working MVP in just 48 hours alongside a team of developers.

�� What we’re building

A lightweight AI-powered tool that:

• Predicts key physicochemical properties of agrochemicals
• Compares candidates against our internal dataset of successful/unsuccessful combinations
• Classifies them into “Closer to YES” / “Closer to NO”
• (Optional) Explains predictions using LLMs

This is not about replacing experiments — it’s about prioritizing what to test next, dramatically accelerating R&D cycles.

�� Who we’re looking for

A Data Scientist with strong chemistry intuition, ideally with experience in:

• Cheminformatics (RDKit, DeepChem, molecular descriptors, fingerprints)
• Working with chemical datasets (PubChem or similar)
• Similarity models (Tanimoto, clustering, k-NN, Random Forest, etc.)
• Translating scientific problems into simple, testable models

Bonus if you:

• Have worked on small datasets / low-data ML problems
• Are comfortable building scrappy MVPs fast
• Are excited about applying AI to real-world, high-impact problems

⚡ What’s the opportunity

• Join us during the CodeLaunch finals hackathon (48h sprint)
• Work directly with founders and a dev team on a real product with real data
• Be part of a project at the intersection of AI, chemistry, and climate impact

�� And if there’s a good fit, this can evolve into an ongoing collaboration to build this into a core capability at Unibaio.

�� Why this matters

Agriculture depends heavily on chemical inputs — but most of them are inefficiently used.
At Unibaio, we’re already showing that we can reduce usage by up to 50% in real-world conditions.
With AI, we aim to scale that impact faster, unlocking better combinations, faster validation, and ultimately reducing environmental impact at global scale.

Minimum Hours per Week:

4-6 hours per week

Duration:

1-2 months