Staff Data Scientist, Pricing

<h3><span style="color: #ba372a;">Help Us Build The Future of Travel</span></h3> <p>At Airalo, we're making it easier for people to stay connected wherever they travel. As the world's first eSIM store, we help millions of travelers access affordable mobile data in 200+ countries and regions around the world.</p> <p>Today, we're a team of 400+ people across 60+ countries, building a product used by travelers every day. We've grown quickly, but we've worked hard to keep what matters: trust, ownership, and the freedom for people to do great work without unnecessary layers or bureaucracy.</p> <p>We're fully remote by design, genuinely global, and united by a shared mission to make travel simpler for everyone.</p> <h3><span style="color: #ba372a;">Your Next Destination</span></h3> <ul> <li><strong>Location:</strong> Remote.</li> <li><strong>Contract:</strong> Full-time, permanent.</li> <li><strong>Benefits:&nbsp;</strong>Learn more about our benefits here in this link - <a href="https://airalo-public.notion.site/Benefits-25396a97ffca81fb9bc1f0be479f1be3?pvs=74">https://airalo-public.notion.site/Benefits-25396a97ffca81fb9bc1f0be479f1be3?pvs=74</a>&nbsp;</li> <li><strong>Languages:</strong> English is our main working language day to day, so you'll need to be comfortable communicating in it both in meetings and async.</li> </ul> <p>&nbsp;</p> <div><br></div><div> <p>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.</p> <p>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.</p> <p>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.</p> </div><br><strong>What You'll Do:</strong><br><div> <li> <p>Build and own the demand and elasticity modelling capability, quantifying how price affects volume across destination, duration, data tier, and customer segment.</p> </li> <li> <p>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.</p> </li> <li> <p>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.</p> </li> <li> <p>Design and analyse pricing experiments with statistical rigour, and apply causal methods where clean randomisation isn't possible.</p> </li> <li> <p>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.</p> </li> <li> <p>Partner with Commercial and Finance to connect pricing decisions to margin and revenue, and to size opportunities before we commit.</p> </li> <li> <p>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.</p> </li> </div><br><strong>What You'll Bring:</strong><br><div> <li> <p>7+ years in data science, quantitative economics, or applied research, including pricing, monetisation, or marketplace economics work that demonstrably changed decisions.</p> </li> <li> <p>Experience with dynamic or algorithmic pricing systems in production.</p> </li> <li> <p>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.</p> </li> <li> <p>Hands-on machine learning experience across the full lifecycle, from feature engineering and model selection through to deployment</p> </li> <li> <p>Familiarity with optimisation and decision-science methods that turn predictions into recommended actions, whether through constrained optimisation, bandits, or reinforcement learning approaches.</p> </li> <li> <p>Proven experimentation expertise, having designed and defended experiments with a clear view on decision frameworks and common failure modes.</p> </li> <li> <p>Experience building analytical capability where none existed before, turning raw data and a business question into something a commercial team uses repeatedly.</p> </li> <li> <p>Strong data modelling instincts, thinking in reusable definitions and single sources of truth rather than standalone analyses.</p> </li> <li> <p>The ability to move comfortably between financial, product and operational data an