Senior Data Scientist - Pricing & Revenue Growth Management
M
MuttdataPricing analytics
Employment type: Remote - LatamFull-TimeSenior
Salary not disclosed
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Job Details
- Experience
- 4+ years of experience as a Data Scientist
- Required Skills
- PythonSQLMachine LearningDatabricks
Requirements
- Bring 4+ years of experience as a Data Scientist, ideally in pricing, Revenue Growth Management, demand modeling, forecasting, or optimization.
- Have advanced proficiency in Python and SQL.
- Demonstrate strong knowledge of applied statistics, econometrics, and supervised machine learning.
- Bring hands-on experience with price-elasticity modeling, demand modeling, price-response curves, or equivalent approaches.
- Understand own-price and cross-price elasticity.
- Have experience with regression models, regularization techniques, and statistical inference.
- Bring a strong background in time-series analysis and forecasting.
- Have experience with mathematical optimization, scenario modeling, and sensitivity analysis.
- Be proficient in feature engineering using pricing, volume, seasonality, and commercial variables.
- Have experience designing experiments, performing backtesting, and conducting statistical validation.
- Bring hands-on experience with Databricks, Spark/PySpark, and MLflow.
- Have experience working with sales data involving pricing, volume, products, channels, geographies, and time dimensions.
Responsibilities
- Design, develop, and optimize Pricing and Revenue Growth Management models.
- Build and validate own-price and cross-price elasticity models.
- Develop predictive models using machine learning, statistical methods, and econometric techniques.
- Perform time-series analysis, demand forecasting, and mathematical optimization.
- Engineer features from pricing, sales volume, seasonality, product, channel, and market data.
- Design experiments, backtesting, sensitivity analyses, and statistical validations.
- Evaluate model robustness, temporal stability, potential biases, and statistical assumptions.
- Develop pricing scenarios and simulations to assess commercial strategies.
- Collaborate with business stakeholders, Data Engineers, and Machine Learning Engineers.
- Present analytical findings and actionable recommendations to technical and non-technical stakeholders.
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