#1 - Senior Data Scientist - Databricks - Pricing & Revenue Growth Management

Muttdata

16d•Remote

About The Position

Muttdata is seeking a Senior Data Scientist for a remote-first data products and machine learning startup. The role supports a leading multinational beverage company based in Mexico City and focuses on designing, developing, and improving Pricing and Revenue Growth Management (RGM) models. The scientist will use data on demand, pricing, product portfolios, sales channels, and consumer behavior to produce insights that support strategic commercial decisions. The role combines machine learning, statistics, econometrics, and business expertise to build predictive models, analyze price-demand relationships, estimate price elasticities, and optimize pricing strategies. The Senior Data Scientist will work with business stakeholders, Data Engineers, and Machine Learning Engineers to deliver scalable, statistically robust solutions and communicate actionable recommendations to technical and non-technical audiences.

Requirements

At least 4 years of experience as a Data Scientist, ideally in pricing, Revenue Growth Management (RGM), demand modeling, forecasting, or optimization.
Advanced proficiency in Python and SQL.
Strong knowledge of applied statistics, econometrics, and supervised machine learning.
Hands-on experience with price elasticity modeling, demand modeling, price-response curves, or equivalent approaches.
Solid understanding of own-price and cross-price elasticity.
Experience with regression models, regularization techniques, and statistical inference.
Strong background in time series analysis and forecasting.
Experience with mathematical optimization, scenario modeling, and sensitivity analysis.
Proficiency in feature engineering using pricing, volume, seasonality, and commercial variables.
Experience designing experiments, performing backtesting, and conducting statistical validation.
Hands-on experience with Databricks, Spark/PySpark, and MLflow.
Experience developing and validating statistical and machine learning models that support business decision-making.
Experience working with sales datasets involving pricing, volume, products, channels, geographies, and time dimensions.
Ability to build models across multiple levels of granularity and assess their statistical robustness and stability over time.
Strong analytical and problem-solving skills, including the ability to translate business challenges into mathematical and predictive models.
Excellent communication and data storytelling skills, including experience presenting insights to technical and business stakeholders.
Experience collaborating with cross-functional teams, including business stakeholders, Data Engineers, and Machine Learning Engineers.

Nice To Haves

Experience working with Large Language Models (LLMs).
Knowledge of AI agents and agentic systems.
Familiarity with MLOps practices and tools.
Experience deploying and maintaining Data Science and AI solutions in production environments.
Knowledge of job orchestration, computing infrastructure, deployments, monitoring, and maintenance.
Experience working with event data, user tracking, and application tagging.
Ability to collaborate effectively with Product, Engineering, and Business teams.

Responsibilities

Design, develop, and optimize Pricing and Revenue Growth Management (RGM) models to support strategic business decisions.
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 to analyze demand, pricing, and consumer behavior.
Perform time series analysis, demand forecasting, and mathematical optimization to identify business opportunities.
Conduct feature engineering using pricing, sales volume, seasonality, product, channel, and market data.
Design and execute experiments, backtesting, sensitivity analyses, and statistical validations to assess model reliability.
Evaluate model robustness, temporal stability, potential biases, and underlying statistical assumptions.
Develop pricing scenarios and simulations to assess the impact of commercial strategies.
Collaborate with business stakeholders, Data Engineers, and Machine Learning Engineers to translate business challenges into data-driven solutions.
Communicate complex analytical findings and actionable recommendations to technical and non-technical stakeholders.