{"id":118708,"url":"https://alion.io/job/airalo-staff-data-scientist-pricing-2","title":"Staff Data Scientist, Pricing","company":{"id":6192,"name":"Airalo","domain":"airalo.com","url":"https://alion.io/company/airalo","size_band":"201-500","is_staffing_agency":false,"employer_type":"direct","is_intermediary":false,"listed_via":null,"ats_vendor":"Lever","truth_index":{"grade":"A","score":100,"open_postings":12,"ghost_share":0,"stale_share":0,"repost_share":0,"time_to_fill_p50_days":52,"computed_at":"2026-11-04T06:34:25Z"}},"role":"Data Science","role_family":"Data Science","seniority":"staff","employment_type":"full_time","work_mode":"remote","remote_scope":"stated_countries","remote_scope_basis":"board_field","remote_working_hours":null,"hiring_geo_confidence":"inferred","locations":["United Kingdom","Spain","Germany"],"countries":["GB","ES","DE"],"hiring_countries":["GB","ES","DE"],"hiring_countries_total":3,"salary":{"min":93200,"max":139800,"currency":"GBP","period":"year","gross":null,"usd_annual":187028},"salary_estimate":null,"experience_years_min":7,"visa_sponsorship":false,"relocation_package":false,"has_equity":false,"technologies":[{"name":"Machine Learning","optional":false},{"name":"Python","optional":false},{"name":"Reinforcement Learning","optional":false},{"name":"SQL","optional":false},{"name":"dbt","optional":true}],"status":"live","first_seen_at":"2026-08-18T07:53:55Z","employer_posted_date":"2026-08-18","last_verified_at":"2026-11-04T22:18:15Z","board_verified":true,"closed_at":null,"days_open":78,"trust":{"level":"ok","repost_count":1,"flags":["company_stale"],"days_open":77},"description":"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.\nYou 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.\nThe 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.\nWhat You'll Do:\nBuild and own the demand and elasticity modelling capability, quantifying how price affects volume across destination, duration, data tier, and customer segment.\n\nDevelop 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.\n\nMove us from predictive to prescriptive by building the optimisation layer that turns forecasts and elasticities into recommended prices under margin, competitive and partner constraints.\n\nDesign and analyse pricing experiments with statistical rigour, and apply causal methods where clean randomisation isn't possible.\n\nDefine pricing within our data ecosystem, owning the governed definitions of price, cost and package economics that reporting, analysis and product surfaces all read from.\n\nPartner with Commercial and Finance to connect pricing decisions to margin and revenue, and to size opportunities before we commit.\n\nSet 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.\n\nWhat You'll Bring:\n7+ years in data science, quantitative economics, or applied research, including pricing, monetisation, or marketplace economics work that demonstrably changed decisions.\n\nExperience with dynamic or algorithmic pricing systems in production.\n\nAn 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.\n\nHands-on machine learning experience across the full lifecycle, from feature engineering and model selection through to deployment\n\nFamiliarity with optimisation and decision-science methods that turn predictions into recommended actions, whether through constrained optimisation, bandits, or reinforcement learning approaches.\n\nProven experimentation expertise, having designed and defended experiments with a clear view on decision frameworks and common failure modes.\n\nExperience building analytical capability where none existed before, turning raw data and a business question into something a commercial team uses repeatedly.\n\nStrong data modelling instincts, thinking in reusable definitions and single sources of truth rather than standalone analyses.\n\nThe ability to move comfortably between financial, product and operational data and connect the analysis to a financial outcome.\n\nGenuine partnership instincts, translating between analytical rigour and commercial reality so that Commercial and Product come to you early rather than after the decision.\n\nFluency in the Python ML stack alongside strong SQL, with the engineering hygiene to hand over code that others can run and maintain.\n\nExcellent communication skills, including the ability to make a methodological argument to people who won't check your standard errors.\n\nA self-starter mindset that thrives in ambiguity and brings structure without waiting for permission.\n\nComfort using AI tools to augment analytical work, with a point of view on where they help and where they don't\n\nNice to have:\nExperience in marketplace, telecom, travel, or subscription/usage-based businesses.\n\nBayesian or hierarchical modelling for sparse segments and long-tail SKUs.\n\nCompetitive price response modelling, or working with scraped competitor pricing data.\n\nFamiliarity with dbt, LightDash, or similar semantic/BI layers.\n\nExperience designing semantic or metric layers consumed by both analytics and production systems.\n\nExperience working in cross-functional teams spanning commercial, finance and product disciplines.","description_format":"text","description_chars":4497,"description_truncated":false,"requirements":{"experience_years_min":7,"management_years_min":null,"team_size_min":null,"manages_managers":false,"education":null,"security_clearance":false,"languages":[]},"benefits":[],"hiring_locations":[{"name":"United Kingdom","iso":"GB","kind":"country"},{"name":"Spain","iso":"ES","kind":"country"},{"name":"Germany","iso":"DE","kind":"country"}],"hiring_excludes":[],"relocation_offered":false,"industries":["MVNO","Travel 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