Lead / Senior Data Scientist – Pricing & Revenue Optimization (m/f/d)

About the Role

This is the first Data Science hire for PUMA, numa’s in-house Revenue Management System.

Your mission is simple and demanding: find the gold nuggets in the data and convert them into production-ready models that drive measurable revenue impact — fast.

You will work closely with the PUMA Product Manager, who owns product vision, roadmap, and stakeholder alignment. Your role is to solve the hard analytical problems, validate hypotheses quantitatively, and translate them into models that can be automated and scaled.

This is not a research role and not a reporting role. It is applied decision science in a real-world, high-noise environment, with clear success metrics and tight feedback loops.

What You’ll Do

• Design, implement, and iterate on pricing, demand forecasting, and elasticity models under the guidance of the Product Manager.
• Analyze large, noisy datasets to identify high-impact pricing levers (day-of-week effects, events, demand compression, competitor pricing, LOS controls, overbooking effects).
• Build models that balance statistical rigor with operational constraints — accuracy and robustness matter more than theoretical elegance.
• Support fast experimentation cycles by defining model changes, success metrics, and analytical readouts.
• Own post-experiment analysis: causal inference, uplift estimation, and clear, data-backed recommendations to the PM.
• Prepare models for production use: stability checks, explainability, edge-case handling.
• Contribute to logic for event detection, demand shocks, and high-compression periods.
• Translate analytical results into clear, implementable pricing logic for engineers and the Product Manager.

What Success Looks Like (First 12–18 Months)

• Measurable RevPAR uplift versus external RMS benchmarks.
• Forecast error consistently within ±3%.
• Multiple pricing levers live in production, not stuck in analysis.
• Experiments shipping every sprint — not quarterly.

Required Experience & Profile

Must-Have

• 6–10+ years of hands-on experience in data science, analytics, or applied decision science.
• Big Plus: Proven experience in dynamic pricing, revenue optimization, or demand forecasting environments.
• Strong applied background in statistics, econometrics, or a highly quantitative field.
• Solid track record designing and evaluating experiments and causal inference frameworks.
• Ability to work independently on complex analytical problems, with guidance on priorities but not on execution.

Mindset & Traits

• A true data nerd with strong commercial instincts.
• Obsessed with real-world impact, not model elegance.
• Comfortable making decisions under uncertainty.
• Low ego, high ownership, high speed.
• Thinks in expected value, not opinions.

Why Join numa / PUMA

• Work on one of the most commercially impactful problems in hospitality.
• Direct ownership over models that influence hundreds of millions in revenue.
• Minimal hierarchy, fast decisions, and real autonomy.
• A Product partner focused on learning speed and outcomes, not vanity metrics.