Senior Data Scientist , Pricing Strategy

About the role

We are building a Pricing function that decides price with evidence rather than intuition. As Senior Data Scientist, Pricing, you will own the analytical and modelling core of that function: the dynamic pricing models, the price-elasticity forecasting, the causal-measurement toolkit, and the experimentation methodology that tell us how customers actually respond to price. You will work across all of Klook's markets and booking channels.

This is a builder role. You will design and ship the production analytical stack (demand and elasticity forecasting and the logic behind our dynamic-pricing engine), not just produce one-off analyses. You will report directly to the Director of Pricing and partner closely with Strategy & Ops, Platform & Intelligence, Vertical Planning, Product, and Marketing.

What you'll do

1.. Price elasticity, forecasting & causal measurement

Build price-elasticity models and demand forecasts by product, segment, and market, anticipating market trends and customer behaviour, and translate elasticity curves into concrete price recommendations and increment sizes.

Develop advanced measurement methods (difference-in-differences, synthetic control, and other quasi-experimental techniques) to isolate the true causal impact of price and promotion changes where clean A/B tests aren't feasible.

Quantify uncertainty honestly (confidence intervals, sensitivity checks) so leadership knows how much to trust each number.

2. Experiment design & execution

Rebuild and run a rigorous experimentation methodology: proper hypotheses, minimum detectable effect (MDE), power and sample-size calculations, guardrail metrics, and clear stopping rules, moving us beyond fixed-window tests.

Design and execute pricing experiments (A/B and switchback/geo tests) to measure lift, reading backward from elasticity curves to choose meaningful price increments.

Establish standards and templates so experimentation scales across the team and results are trusted by P&L owners approving rollouts.

3. Data exploration & insight

Conduct deep exploratory analysis across pricing, competitor, booking, and margin data to surface opportunities and inform pricing strategy.

Work with Platform & Intelligence on crawling, mapping, and data enablement to ensure a clean, transparent, trustworthy source of truth for pricing decisions.

4. Dynamic pricing model development

Design, build, and productionise dynamic pricing models that optimise price across products, channels, and markets, balancing conversion, revenue (top line), and gross profit (bottom line).

Translate distinct pricing hypotheses into models: for example, competitor-anchored pricing for commodity-like products, and value-based pricing for others.

Partner with engineering and the pricing-engine owners to move models from prototype to a reliable, monitored production system.

Go beyond prediction to prescription: recommend the optimal price for each product given business objectives and constraints such as margin floors, competitive position, and inventory.

Continuously evaluate, backtest, and refine models in production: monitor accuracy and drift and retrain so pricing stays reliable as markets move.

5. Cross-functional partnership

Collaborate with Vertical Planning, Product, Marketing, and Data to translate findings into strategic recommendations and actionable rollout plans.

Support decisions on promotion and marketing spend by measuring incrementality and lift, so budget flows to what demonstrably works.

6. Communication & influence

Communicate insights and results clearly to senior stakeholders, up to VP and C-suite, translating technical methods into business impact and defensible recommendations.

Guide data-driven decision-making across pricing, promotion, and budgeting through crisp written analysis and executive-ready narratives.

What you'll bring

Must-have

A Master's or PhD in a quantitative field (statistics, economics, computer science, or data science) is preferred. Ability to deliver matters more than credentials, so equivalent hands-on experience is equally valued.

5+ years in data science / quantitative analytics, with real ownership of pricing, revenue management, demand forecasting, or causal-measurement problems (ideally in a marketplace, e-commerce, travel/OTA, or platform business).

Causal inference depth: hands-on experience with difference-in-differences, synthetic control, instrumental variables, or uplift modelling, and a clear grasp of their assumptions and failure modes.

Experimentation rigour: you can design a clean experiment from scratch (power/MDE, randomisation unit, guardrails, sequential/peeking pitfalls), not just read a dashboard.

Modelling: price-elasticity estimation, demand forecasting, and applied statistical/ML modelling (regression, time-series, classification, clustering) for pricing or demand.

Tooling: strong Python and SQL; comfortable working with large, messy data end-to-end.

Communication: a track record of turning analysis into decisions with non-technical and executive stakeholders.

Nice to have

OTA / travel-industry experience, and familiarity with what makes travel pricing distinct: perishability, seasonality, rate parity, and supplier dynamics.

Experience productionising models and partnering with engineering / MLOps to ship and monitor a pricing or recommendation engine.

Comfortable in a cloud data-warehouse and compute environment (for example BigQuery) with standard Python data-science libraries.

Exposure to competitive-intelligence data (crawling, price mapping) and to bulk-buy / inventory economics.

Fluency with LLMs and modern AI tooling, including the ability to build reusable AI skills, agents, and workflows that lift the whole team's productivity, on top of speeding up your own analysis, specs, and communication.

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