Data Scientist

Overview

Source.ag, we're on a mission to power a sustainable future by leveraging cutting-edge A.I. in greenhouse technology to deliver more fresh produce to the world. Our values drive everything we do to support this mission; it’s the way we work, collaborate, and achieve our goals:

🧑🌾 All In for our Growers: We work closely with growers, prioritizing their needs.

🌈 Strength through Diversity: We embrace different perspectives and backgrounds, fostering a culture of mutual respect, inclusion, and collaboration.

🌟 Learn-Adapt-Succeed: We grow through continuous feedback, learning, and dedication.

🚀 Driven to Deliver: We are dedicated to delivering impactful results.

🌱 Plant Solutions
:
We plant solutions that shape the future of food production.

Our autonomous greenhouse control product is at the heart of this mission, specifically our irrigation autopilot. Imagine an intelligent system that precisely delivers the optimal amount of water and nutrients to plants, exactly when they need it, every single day. This isn t just about efficiency; it s about driving stronger plants, higher yields, and a more resilient global food supply.

This system is far from simple. Think of it as the adaptive cruise control of a modern car, but instead of steering vehicles, you re steering plants. It s a complex, dynamic environment with numerous constraints, conflicting objectives, noisy sensor data, and significant time lags.

We re looking for a bright, pragmatic Data Scientist who thrives on complexity and possesses a first-principles approach with an unshakeable engineering mindset
. You are a force multiplier who values collective achievement over solo virtuosity and thrives in a team where the best idea wins, not the loudest voice.

You'll be instrumental in bringing the world s best environmental control systems to growers globally. If you have a proven track record of building sophisticated engineering systems where data is inherently messy, and nothing is linear, and you re eager to make a tangible, positive impact on sustainable food production, this is your opportunity.

Having launched our Irrigation Control product in April 2024, we are at the forefront of AgTech innovation. We ve amassed a rich dataset and deep domain expertise, providing an unparalleled foundation to accelerate and enhance our solutions.

In our Control team
, you ll be embedded within a highly collaborative environment, working side-by-side with Software Engineers, Product Managers, and AI Solution Specialists. Together, you ll explore, design, ship, and continuously support new models and products in live operations. What makes this role truly unique is the direct collaboration with Plant Scientists and expert growers, allowing you to integrate cutting-edge biological knowledge and real-world practices directly into your work.

• Architect and deploy robust logic pipelines that run 24/7, autonomously controlling irrigation in greenhouses worldwide.

• Decisively navigate uncertainty to drive rapid convergence from experimental phases to iterative, production-ready releases, prioritizing tangible real-world impact over pure research.

• Push the boundaries where engineering, data science, and plant science converge to develop state-of-the-art models for innovative customer products.

• Own modelling initiatives end-to-end
  , from initial brainstorming and conceptualization to packaging, deployment, and ongoing monitoring.

• Masterfully combine data, deep domain knowledge, scientific principles, grower expertise, statistics, and advanced AI/ML tooling to solve critical, real-world business problems.

• Embody Source s mission of building the world s best Data & AI solutions for growers, directly impacting global food security.

• MSc or PhD in a Science, Technology, Engineering, or Math discipline

• Minimum 4 years of experience in a relevant discipline

• A relentless passion for problem-solving
  , with an intrinsic drive to find solutions and see them through to successful implementation.

• Exceptional eagerness to dive deep into complex domain knowledge
  , understanding that it s paramount to the success of our models.

• A reflective and iterative mindset
  ,…