Build and own the demand and elasticity modelling capability, quantifying how price affects volume across destination, duration, data tier, and customer segment.

Develop machine learning models for demand forecasting and willingness-to-pay, and take them from exploration through to production with the monitoring and retraining that keeps them honest.

Move us from predictive to prescriptive by building the optimisation layer that turns forecasts and elasticities into recommended prices under margin, competitive and partner constraints.

Design and analyse pricing experiments with statistical rigour, and apply causal methods where clean randomisation isn't possible.

Define pricing within our data ecosystem, owning the governed definitions of price, cost and package economics that reporting, analysis and product surfaces all read from.

Partner with Commercial and Finance to connect pricing decisions to margin and revenue, and to size opportunities before we commit.

Set the analytical standard for pricing at Airalo, from what counts as evidence through to how a model or recommendation gets validated before it influences live pricing.

7+ years in data science, quantitative economics, or applied research, including pricing, monetisation, or marketplace economics work that demonstrably changed decisions.

Experience with dynamic or algorithmic pricing systems in production.

An advanced degree in Econometrics, Statistics, Operations Research or similar, or equivalent applied depth in demand estimation and the identification problems that make naive price-quantity regressions wrong.

Hands-on machine learning experience across the full lifecycle, from feature engineering and model selection through to deployment

Familiarity with optimisation and decision-science methods that turn predictions into recommended actions, whether through constrained optimisation, bandits, or reinforcement learning approaches.

Proven experimentation expertise, having designed and defended experiments with a clear view on decision frameworks and common failure modes.

Experience building analytical capability where none existed before, turning raw data and a business question into something a commercial team uses repeatedly.

Strong data modelling instincts, thinking in reusable definitions and single sources of truth rather than standalone analyses.

The ability to move comfortably between financial, product and operational data and connect the analysis to a financial outcome.

Genuine partnership instincts, translating between analytical rigour and commercial reality so that Commercial and Product come to you early rather than after the decision.

Fluency in the Python ML stack alongside strong SQL, with the engineering hygiene to hand over code that others can run and maintain.

Excellent communication skills, including the ability to make a methodological argument to people who won't check your standard errors.

A self-starter mindset that thrives in ambiguity and brings structure without waiting for permission.

Comfort using AI tools to augment analytical work, with a point of view on where they help and where they don't

Experience in marketplace, telecom, travel, or subscription/usage-based businesses.

Bayesian or hierarchical modelling for sparse segments and long-tail SKUs.

Competitive price response modelling, or working with scraped competitor pricing data.

Familiarity with dbt, LightDash, or similar semantic/BI layers.

Experience designing semantic or metric layers consumed by both analytics and production systems.

Experience working in cross-functional teams spanning commercial, finance and product disciplines.