Description:

Multiplica Talent is seeking a Data Scientist with experience in Marketing Mix Modeling (MMM) to join their advanced analytics team.
The ideal candidate will be responsible for developing econometric models to measure the impact of marketing investments, optimize budgets, and generate actionable insights that drive business growth.
Responsibilities include designing, developing, and improving MMM models to evaluate the impact of various marketing channels such as TV, digital, social media, OOH, and radio.
The candidate will apply statistical and econometric methodologies, including Bayesian regression, time series models, and machine learning, for marketing impact attribution.
The role involves cleaning, processing, and analyzing large volumes of data from multiple sources, including media data, sales, weather, and market trends.
Collaboration with marketing, finance, and strategy teams is essential to translate findings into investment recommendations and campaign optimization.
The candidate will implement scalable models and automate reports and dashboards for result visualization.
Staying updated on marketing analytics trends, data privacy, and advanced approaches like causal inference and experimentation is required.

Requirements:

A degree in Data Science, Statistics, Economics, Mathematics, Engineering, or related fields is required.
The candidate must have 3+ years of experience in Data Science, focusing on econometric models and marketing analysis.
Experience in Marketing Mix Modeling (MMM) and media attribution is essential.
Proficiency in statistical and programming tools such as Python (statsmodels, pymc, scikit-learn), R (lm, brms, bayesmodels), and SQL is required.
Experience with Bayesian models and frameworks like PyMC3, Stan, or TensorFlow Probability is a plus.
Knowledge of causality techniques such as Propensity Score Matching, Difference-in-Differences, Synthetic Control, and A/B Testing is necessary.
Experience with data visualization platforms like Tableau, Power BI, or Looker is required.
Strong communication skills are needed to present insights clearly to non-technical stakeholders.
Familiarity with privacy regulations (GDPR, CCPA) and modeling in data-limited environments is important.