Senior Data Scientist, Pricing Performance
Measure the impact of our pricing algorithm, customer by customer, and turn what you find into the next improvement.
About Walkway
Walkway helps tour, activity and attraction operators grow revenue through dynamic pricing and competitive intelligence.
We combine competitor data, booking patterns, seasonal trends and demand signals to deliver practical pricing recommendations and automation. Operators stay in control: they review every change, or flip on Auto Pilot and let it run.
Walkway has already powered more than 10 million live operator prices and $40 million in gross booking value, helping businesses across the world put smarter pricing into practice.
Our team brings experience from Airbnb, Viator, Regiondo, and KAYAK, alongside expertise in revenue strategy, data science, engineering and product design.
The role
A walking tour, a museum and a boat ride don't behave the same way. That's why our algorithm adapts to each product, and why we measure its impact product by product, not on average. Our customers are getting bigger and closer to their data, and they rightly expect every number we share to hold up.
We're hiring a Senior Data Scientist to own pricing performance end to end: the method behind every number we share, the deep dives when a customer asks why prices moved, and the bridge between our leadership team and our data scientists. You'll free our algorithm team to focus on building, and give leadership the evidence to set strategy.
Walkway is a US company, so this is a full-time role under a contractor agreement. You're based in Europe, ideally in Spain, Portugal or Italy, where most of the team is, and you work European hours.
This role reports directly to the CTO.
What you'll do
1. Performance measurement
- Own one agreed method to measure the algorithm's impact, used for every performance number we share. Build it with our algorithm team, freeze it, and sign off every customer report and case study.
- Estimate the treatment effect of our pricing, not a before/after: build the counterfactual (matched controls, holdouts, partial exposure) and identify and handle the confounders that move with it, such as seasonality, demand shifts and booking lead time.
- Run a weekly read on our top operators: what's driving results for each product, and where there's still revenue to capture.
2. Deep dives and the algorithm
- When a customer or our customer success team asks why prices moved the way they did, dig in: the product setup, the data, and the demand, capacity and market signals behind each price. Answer with evidence, and bring our algorithm team the cases worth a closer look.
- Turn what you find into hypotheses for the algorithm team: the mix of factors, how they interact, the direction and size of price moves, and where new signals could help.
- Validate the algorithm's own impact estimates against realized outcomes.
3. Leadership and product insight
- Be the bridge between our leadership team and our data scientists. Bring what we're learning about performance to leadership in plain language, so strategy rests on evidence.
- Identify which kinds of products respond best to dynamic pricing and why, so the company knows where to grow next.
- Own usage analytics: Auto Pilot adoption, feature usage, the paths that lead to success. This feeds our product decisions.
4. Automation and metrics
- Build the AI agents and automated reports that let one person cover a growing customer base.
- Own our metric definitions in the data layer, with our data engineer.
What you'll build, and what you won't
You'll build plenty of models: counterfactuals, treatment-effect estimates, product segmentations, and automated reads that run every week. What you won't build is the pricing algorithm itself; our algorithm team owns that. You'll start in the trenches with them, learning what the algorithm does and why, then agree with them on the measurement framework we put in front of customers. Real-world pricing data is never perfectly clean, so we want measurement that's rigorous, consistent and honest about what it can and can't say.
Our stack (you don't need all of it)
- BigQuery, dbt, Python, Postgres
- Mixpanel, Metabase, Mage
- Our own AI analytics agent, GitHub, Linear
What we're looking for
- Impact measurement at the core. 5+ years in data science or analytics, with impact measurement or experimentation at the core of your work, ideally in pricing, revenue management or marketplaces (travel, hospitality, mobility, e-commerce).
- Causal inference on messy, real-world data. Treatment effects, counterfactuals and confounders, with difference-in-differences, matched comparisons, synthetic controls, holdouts and uplift in your toolbox. You know when a before/after comparison misleads, and you're comfortable saying "we don't know yet" when the sample is small.
- Strong SQL and Python. BigQuery, dbt, and building the model you need instead of waiting for someone else to.
- Comfortable in code. You can read a Python codebase well enough to follow how a price was built, factor by factor.
- Business sense. You can explain a number to our CEO or a customer without jargon, in excellent English, and you'll challenge any of us, the CTO included, when the data says so.
- AI-native. LLMs and agents are already part of how you analyze, not a side experiment.
- Answers, not dashboards. You care about the decision a number leads to.
Nice to have: price elasticity or demand modeling; product analytics and funnel analysis (Mixpanel, Amplitude or similar); Bayesian methods and small-sample inference; experience in tours and activities, travel or hospitality; Spanish, Portuguese or Italian.
Why join Walkway?
Own the proof behind the product.
You'll own how we measure the impact of an award-winning AI pricing product used by operators around the world.
Work where data science, product and leadership meet.
Your findings shape both the algorithm and the strategy, with a pragmatic, low-ego team that ships fast.
A role that grows with the company.
The scope will evolve as we grow. If that excites you more than it worries you, you'll fit right in. Fully remote, with in-person team gatherings during the year, travel covered.
How to apply
Send your CV with your LinkedIn or GitHub and a short note on an impact measurement you designed: the question, the method, what you concluded and how confident you were.