Monq
Senior Data Scientist/Researcher
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About the Role
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.
What You'll Be Doing
Price Intelligence and Forecasting
- 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
ML Skills We're Looking For
This role sits at the intersection of classical econometrics and modern machine learning. You should be genuinely strong across most of the following:
- 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
The Stack
- You'll have significant input into tooling choices: experiment tracking, feature stores, deployment infrastructure
- We ship on AWS and are building the MLOps layer on the go — you'll help define it
- We use Cursor and Claude Code as part of the daily workflow, not as a novelty
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What You'll Need
Essential Experience
- 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
Nice to Have
- 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
Compensation and Benefits
At Monq, we recognise that we're building category-defining technology in a massive market. We offer:
Competitive Package
- Significant equity stake — play a real part in Monq's potential to capture a $4.2T market
- Bi-annual performance bonuses tied to successful pilot deployments and customer outcomes
Work Environment
- 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
Career Growth
- 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
Others
- 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)
Our Interview Process
We run a focused interview process designed for senior data science talent:
Screening Call (30 minutes)
- Discussion of your data science background and forecasting experience
- Overview of Monq's price intelligence challenges and the scope of the role
- Alignment on career goals and role expectations
Technical Assessment — Home Task and Debrief (45 minutes)
- Take-home task: given a real-world commodity dataset, design a forecasting approach — covering model selection, validation strategy, uncertainty quantification, and how you'd communicate the output to a procurement team
- Walk through your approach with Monq's data and engineering team
- Discussion of your modelling decisions and tradeoffs
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Problem Solving Interview (45 minutes)
- A live scenario based on a real forecasting challenge Monq has encountered — a regime shift in a commodity market, an unreliable data source, or a stakeholder who doesn't trust the model
- Assessment of how you diagnose the problem, adapt your approach, and communicate under uncertainty
- Discussion of the most technically demanding forecasting project you have personally owned end to end
Bar Raiser Interview and On-site Collaboration (half a day)
- Deep technical conversation with one of Monq's co-founders on the most complex modelling problem you have solved and what you would do differently
- Working session with Monq's data and engineering teams on a current price intelligence challenge
- Meeting with procurement domain experts to understand how predictions feed into live negotiation decisions
- Assessment of your ability to operate at the intersection of rigorous research and production-grade engineering
About Monq
Monq is building the first AI platform specifically designed for strategic procurement negotiation. We're creating a blue ocean in a $4.2 trillion market that has been ignored by existing AI solutions. Our team brings together deep technical expertise in AI with proven experience in enterprise software and procurement.
We're backed by forward-thinking investors who understand the massive opportunity in applying advanced AI to high-value B2B negotiations. With several major enterprises joining us, we're positioned to become the category-defining platform for AI-powered procurement. We are founded and backed by Revolut and HSBC executives.
We're a small, flat team. We use AI tools not as a novelty but because they make us better and faster. We value simplicity, ownership, and shipping — and we're looking for people who hold themselves to high standards while staying pragmatic about what matters right now.
Why This Matters: Every 1% improvement in strategic procurement represents a $42 billion market impact. We're not just building software — we're creating the future of how businesses negotiate their most critical deals.
Equal Opportunities Statement
Monq is committed to creating a diverse and inclusive workplace and is proud to be an equal-opportunity employer. All qualified applicants will receive consideration for employment without regard to race, colour, religion, gender, gender reassignment, marital or civil partnership status, age, disability, pregnancy or maternity, or any other basis as protected by the Equality Act 2010.
We actively encourage applications from people with diverse backgrounds and experiences to join this multicultural, ambitious team.
Accommodations for Applicants with Disabilities
Monq is dedicated to providing reasonable accommodations to job applicants with disabilities. If you require any adjustments during the recruitment process, please indicate this in your application or contact us directly at recruitment@monq.io.
Important Notice for Candidates
Job scams are on the rise, and fraudsters sometimes impersonate companies like Monq to target applicants. Please keep these guidelines in mind when applying for any of our open roles:
- Apply only through official Monq channels. We post roles solely on our own careers page and verified partner job boards such as LinkedIn. We don't use third-party
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