Senior Data Scientist/Researcher

The Problem

Every major enterprise procurement deal - a mining company locking in steel supply, a manufacturer negotiating energy contracts, a retailer securing food commodities - lives or dies on one question: what will this cost six months from now? Today, that question is answered with spreadsheets, gut instinct, and analyst reports written days after markets have already moved. Billions of dollars in value are left on the table - or surrendered across the negotiating table - because procurement teams are flying blind on price. At Monq, we’re building AI agents that negotiate high-value enterprise contracts - and we’re expanding what that platform can do. The next frontier is price intelligence: giving procurement teams the foresight to know what a deal should cost before they even sit down to negotiate. That’s what you’ll build.

What You’ll Build

This is genuinely 0-to-1. There is no existing model, no data pipeline, no baseline to iterate on. You’re starting from a blank sheet - which means you’ll need to be comfortable with ambiguity, scrappy about data sourcing, and confident making architectural decisions without a committee to approve them. In return, everything you build will matter immediately, and you’ll own it completely. Concretely, you will:

• Build multivariate commodity price prediction models from scratch. You’ll work across energy, metals, agricultural inputs, and industrial materials — constructing models that capture the full complexity of cross-commodity dependencies, supply chain dynamics, macroeconomic signals, and geopolitical risk.
• Own the full modelling lifecycle. Feature engineering, model selection, validation strategy, uncertainty quantification, production deployment.
• Design forecasting architectures that go beyond the obvious.
• Integrate alternative data sources. Satellite imagery, shipping data, weather signals, procurement index feeds, news sentiment.
• Shape how predictions become decisions. The end goal is for your models to inform what Monq recommends inside live procurement negotiations.
• Bridge research and engineering to ship production-grade systems.

You Might Be a Fit If

• You have 6+ years of experience in applied data science or quantitative research, with a strong track record in forecasting or time series modelling in production environments.
• You’ve worked on commodity, energy, or financial market price prediction.
• You’re fluent in multivariate modelling: VAR/VECM, Bayesian hierarchical models, factor models, LSTM/transformer-based temporal architectures.
• You’re rigorous about uncertainty.
• You’re comfortable working with messy, heterogeneous, real-world data.
• You can write production-quality Python and know how to deploy models.
• You care about impact, not just accuracy metrics.

Nice to Have

• Experience with causal inference methods applied to market dynamics.
• Familiarity with procurement indices and how to incorporate forward curve data.
• Experience building real-time or near-real-time inference pipelines at scale.
• Background in operations research or supply chain optimisation.
• Exposure to LLMs as signal sources.

ML Skills We’re Looking For

This role sits at the intersection of classical econometrics and modern machine learning. You don’t need to be a world-class expert in every area below - but you should be genuinely strong across most of them and honest about where you want to grow.

• Supervised understanding of when tree-based models outperform neural approaches on structured data, and vice versa.
• Strong intuition for regularisation, hyperparameter tuning, and avoiding leakage in time series cross-validation.
• Deep Learning for Sequences.
• Understanding of attention mechanisms and when transformer-based sequence models are worth the complexity cost over simpler recurrent approaches.