obvious.corporate
obvious.corporate

Senior Data Scientist

Munich, Germany (hybrid)
Employee
Data Scientist

We believe in connecting great people with great opportunities.

Our team is currently recruiting for a client in Germany, and we would be delighted to have you explore this opening.

Tasks

  • Designed and implemented advanced frameworks to measure the causal effects of pricing and revenue optimization initiatives.
  • Applied cutting-edge causal inference methods to guide strategic decision-making in revenue management.
  • Structured and analyzed experiments using DiD, RDD, synthetic controls, A/B testing, and Double Machine Learning to evaluate impact and inform policy.
  • Built scalable algorithms and tools for causal estimation, ensuring accuracy and seamless integration into production systems.
  • Collaborated with product managers, data engineers, and software developers to deliver end-to-end solutions powered by causal insights.
  • Stayed current with research in causal analysis while mentoring junior team members and fostering knowledge growth.

Requirements

  • 5+ years of experience in Data Science with specialization in causal inference, particularly in pricing and marketing contexts, including work with sparse and volatile datasets.
  • Demonstrated expertise in designing and deploying causal frameworks to measure and validate the impact of revenue management and pricing strategies.
  • Strong foundation in statistical modeling and advanced causal inference methodologies.
  • Skilled in Double/Debiased Machine Learning (Double ML) for causal effect estimation using combined machine learning models.
  • Proficient in building and interpreting Causal Graphs and Structural Causal Models (DAGs) for robust causal identification.
  • Advanced application of Propensity Score Matching and Weighting to estimate treatment effects in observational studies.
  • Hands-on experience with Instrumental Variables (IV) and Synthetic Control Methods to assess causal impacts in complex environments.
  • Applied Difference-in-Differences (DiD) and Regression Discontinuity Design (RDD) to evaluate causal effects over time.
  • Fluent English. German is a plus

Benefits

  • 28 vacation days
  • Hybrid model with flexible working hours
  • Partner discounts
  • Training programs, external conferences, and internal talks
  • Private health insurance

Apply now!

Updated: 4 days ago
Job ID: 14833495
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obvious.corporate

1-10 employees
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We support organizations to align people, processes, and technology — starting from what works and building toward what’s next.
Because what is obvious is often what drives real…

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