Senior Data Scientist, Pricing & Revenue Growth Management

MuttdataData science

Remote - LatamFull-TimeSenior

Salary not disclosed

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Job Details

Experience: 4+ years of experience as a Data Scientist, ideally working on Pricing, Revenue Growth Management (RGM), demand modeling, forecasting, or optimization.
Required Skills: PythonSQLMachine LearningDatabricks

Have 4+ years of experience as a Data Scientist, ideally in pricing, Revenue Growth Management, demand modeling, forecasting, or optimization.
Bring advanced proficiency in Python and SQL.
Have strong knowledge of applied statistics, econometrics, and supervised machine learning.
Have 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.
Have 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, backtesting, and statistical validation.
Have hands-on experience with Databricks, Spark/PySpark, and MLflow.
Have experience working with sales datasets involving pricing, volume, products, channels, geographies, and time dimensions.

Design, develop, and optimize Pricing and Revenue Growth Management models.
Build and validate price-demand elasticity models, including own-price and cross-price elasticities.
Develop predictive models using machine learning, statistical methods, and econometric techniques.
Perform time series analysis, demand forecasting, and mathematical optimization.
Engineer features using pricing, sales volume, seasonality, product, channel, and market data.
Design and execute 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 on data-driven solutions.
Communicate analytical findings and recommendations to technical and non-technical stakeholders.

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