Lead Data Scientist

We are currently seeking a Lead Data Scientist to join our team. Finding the right person for this critical role is more important to us than filling it quickly. We are looking for a true partner who will have a foundational impact on our company. If our mission and the qualifications below align with your experience, we strongly encourage you to apply.
We are looking for an experienced and entrepreneurial Lead Data Scientist to join our founding team. This is a critical, high-impact role where you will design and build the core machine learning models that power our platform. You will be responsible for the entire data science lifecycle, from architecting forecasting algorithms to deploying optimisation models that help our customers navigate real-world supply chain volatility.
This is a hands-on role for a true data scientist, not a data engineer or a data analyst. You thrive in a data-heavy environment and are an expert at building predictive and prescriptive models to solve complex, high-stakes operational challenges.
Responsibilities

• Lead the design, development, and deployment of machine learning models to solve key supply chain challenges, including demand forecasting, inventory optimisation, logistics/shipping optimisation, and disruption modelling.
• Partner with product, engineering, and leadership to translate complex customer problems into a clear data science roadmap.
• Conduct in-depth exploratory analysis on large, complex datasets (e. g., historical sales, logistics data, weather patterns, market signals) to uncover actionable insights.
• Establish the foundations of data science at the company, implementing best practices for modelling, code standards, and experimentation.
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  Develop novel approaches to quantify and predict the impact of black swan events like port closures, strikes, or extreme weather on customer supply chains.
• Communicate complex findings and the value of our models to both technical and non-technical stakeholders, including key customers.
• Own the end-to-end data science workflow, from feature engineering to model validation and monitoring in a production environment.
  

Requirements

• 6+ years of professional experience in a data science or machine learning role.
• Significant experience working in a fast-paced startup environment.
• You are comfortable with ambiguity, can prioritise for maximum impact, and work autonomously.

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• Proven track record of working with large-scale, data-heavy environments and delivering ML solutions that drive measurable business value.
• Direct experience in supply chain, logistics, manufacturing, or operations is a massive plus.
• Deep expertise in statistical methods and a wide range of machine learning algorithms, particularly in time-series forecasting, optimisation, and anomaly detection.
• Strong proficiency in Python and its core data science libraries (e. g., scikit-learn, Pandas, NumPy, Prophet, optimisation libraries like PuLP or Gurobi).
• Expert-level SQL skills for complex data querying and manipulation.
• Excellent problem-solving skills and the ability to turn ambiguous business challenges into a well-defined analytical framework.
• An M. S. or PhD is preferred in a quantitative field like Operations Research, Industrial Engineering, Computer Science, Statistics, or a related discipline.
  

This job was posted by Giorgio Caldera from Nuel.