Staff Data Scientist

Who you are

• Languages: English is our main working language day to day, so you'll need to be comfortable communicating in it both in meetings and async
• 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

What the job involves

• We're looking for a Staff Data Scientist, Pricing to build the analytical and machine learning capability that pricing runs on, from demand modelling through to the systems that recommend price
• You won't be starting from zero. We've invested in the foundational data and infrastructure that pricing decisions depend on
• What's missing is the capability on top: models the business trusts, experiments that settle pricing questions with evidence, and economics defined once so they hold consistently across Commercial, Product and the customer experience
• The arc of the role runs from measurement to prediction to prescription - understanding how demand responds to price, forecasting how it will respond, and ultimately building the models that recommend the price itself
• 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

Benefits

• Stock Options
• Tools & Technology - we supply what you need to work your best and stay connected
• Remote-work Stipend
• Work From Anywhere
• Yearly Wellness Credit
• Healthcare Benefits
• Airmoney - enjoy an annual Airmoney budget for you and your family to use eSIMs worldwide
• Flexible Time Off
• Training & Development Budget (USD $500 annual)
• Airalo Academy - for 4 nights and 5 days, team members unite in a host country, all expenses covered