Senior Data Scientist/Researcher

Monq•Capon Bridge, WV

56d•PLN 20,300 - PLN 45,800•Remote

About The Position

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 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. Ready to build the intelligence layer that changes how enterprises negotiate? Let's get in touch.

Requirements

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
Experience in commodity, energy, or financial market price prediction — you understand basis risk, seasonality, mean-reversion, and regime shifts
Fluent in multivariate modelling: VAR/VECM, Bayesian hierarchical models, factor models, LSTM/transformer-based temporal architectures
Rigorous about uncertainty — you know the difference between epistemic and aleatoric uncertainty and build that into how you communicate predictions to stakeholders
Comfortable working with messy, heterogeneous, real-world data — incomplete time series, mixed frequencies, structural breaks
Can write production-quality Python and deploy models in a way that engineers can actually build on
Care about impact, not just accuracy metrics — a model that moves a negotiation outcome is worth more than one that wins a Kaggle leaderboard
Supervised & Ensemble Methods — gradient-boosted trees (XGBoost, LightGBM, CatBoost) for tabular forecasting; strong intuition for regularisation, hyperparameter tuning, and avoiding leakage in time series cross-validation
Deep Learning for Sequences — hands-on experience with temporal architectures including LSTMs, GRUs, Temporal Fusion Transformers, N-BEATS, or similar
Probabilistic & Bayesian Modelling — comfort with probabilistic forecasting: quantile regression, conformal prediction, Monte Carlo dropout, or full Bayesian inference via PyMC or NumPyro
Feature Engineering at Scale — lag features, rolling statistics, Fourier transforms for seasonality decomposition, target encoding with temporal leakage guards
Model Evaluation & Validation — walk-forward validation, purged k-fold cross-validation, backtesting under realistic execution constraints
MLOps & Productionisation — experiment tracking (MLflow, W&B), model versioning, feature stores, drift detection, and retraining triggers
Explainability & Interpretability — SHAP values, partial dependence plots, and the ability to explain model behaviour to procurement professionals who need to trust and act on predictions

Nice To Haves

Experience with causal inference methods applied to market dynamics (synthetic control, difference-in-differences, IV)
Familiarity with procurement indices (PPI, ISM, commodity spot/futures markets) 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 — extracting structured market intelligence from unstructured text
Ongoing PhD or track record of published research in a relevant field

Responsibilities

Build multivariate commodity price prediction models from scratch across energy, metals, agricultural inputs, and industrial materials
Own the full modelling lifecycle — feature engineering, model selection, validation strategy, uncertainty quantification, production deployment
Design forecasting architectures beyond the obvious — Gaussian processes, gradient-boosted ensembles, neural state-space models, or hybrid symbolic-statistical approaches
Integrate alternative data sources: satellite imagery, shipping data, weather signals, procurement index feeds, news sentiment
Shape how predictions become decisions — translate probabilistic outputs into something a procurement professional can act on in a live negotiation
Bridge research and engineering to ship production-grade systems — work in close collaboration with our engineering team to take research from notebook to production

Benefits

Significant equity stake
Bi-annual performance bonuses tied to successful pilot deployments and customer outcomes
Remote-first with quarterly team gatherings
Direct collaboration with Fortune 500 procurement teams
Annual team retreat — fully-funded off-site focused on AI innovation and team building
Visa sponsorship available for exceptional candidates who complete our recruitment process
Opportunity to define the future of AI-powered enterprise negotiations
Direct mentorship from experienced AI researchers and enterprise software veterans
No HR organisation
Minimum 30 days of annual leave and a day off in the month of your birthday
Flexible working hours as long as we get the things done
Temporary work from abroad up to 120 days a year (certain limitations apply depending on your nationality/work authorisation status and tax obligations)