Senior Data Scientist

Ambev

. Work within the Revenue squad to optimize coupons, discounts, and campaigns, maximizing purchase conversion.

Posted 9/15/2026full-timeRemote • BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and optimizing predictive models and recommendation strategies using Python and PySpark, with a strong focus on customer behavior analysis and A/B testing methodologies. Proficient in translating analytical insights into actionable business decisions that enhance revenue generation and customer experience.

Highest-signal resume keywords

Data SciencePythonPySparkMachine Learning ModelsA/B Testing

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Predictive ModelingRecommendation TechniquesMarket Basket AnalysisClustering TechniquesCausal InferenceModel ValidationStatisticsCustomer SegmentationDistributed ProcessingData Analysis

Tools & Technologies

AWSAzureFeature StoresReal-Time SystemsExploration–Exploitation Techniques

Certifications & Qualifications

Bachelor's DegreeGraduate Degree in Data Science

Industry Keywords

Consumer DataPersonalizationCampaign OptimizationPrice PerceptionCustomer Behavior

Tech Stack

Tools & technologies

AWSAzureCloudPySparkPython

About the role

Key responsibilities & impact

Work within the Revenue squad to optimize coupons, discounts, and campaigns, maximizing purchase conversion.
Analyze price perception and customer behavior to identify opportunities for improvement.
Balance commercial appeal, customer experience, and revenue generation.
Develop and enhance predictive models for Zé Delivery.
Build and maintain data pipelines and features in PySpark to support models at scale.
Develop product and offer recommendation strategies throughout the customer journey.
Define, run, and analyze A/B tests to evaluate the impact of models and campaigns.
Translate analytical models into business decisions and impact on key metrics.

Requirements

What you’ll need

Bachelor's degree completed.
Experience working with consumer data applied to recommendation and personalization problems.
At least 3 years of experience as a Data Scientist.
Strong experience with Python and PySpark, including distributed processing and handling large volumes of data in a cloud environment (AWS or Azure).
Experience developing machine learning models for recommendation, prediction, and classification.
Knowledge of recommendation techniques, such as matrix factorization (e.g., ALS), neighborhood-based models, and embeddings.
Experience with market basket analysis and association rules (e.g., Apriori, FP-Growth).
Experience with clustering and customer segmentation techniques based on behavior and lifecycle.
Knowledge of causal inference, A/B testing, and incremental impact measurement.
Experience with offline and online evaluation of recommendation models (e.g., NDCG, MAP, uplift, and business metrics).
Solid foundations in statistics, machine learning, and model validation.
Graduate degree in Data Science or a related field (including a master's or Ph.D.) — a plus.
Experience with modern recommendation architectures, embeddings, real-time systems, feature stores, feature serving, and exploration–exploitation techniques — a plus.

Benefits

Comp & perks

Medical Insurance
Dental Insurance
Telemedicine
Life Insurance
Gympass
Discounts on company products
Christmas food basket
Toys for employees' children
Private pension plan
Meal or food allowance
Optional transportation allowance
Attendance bonus equivalent to a 14th salary
Childcare or babysitter assistance
Profit sharing
Professional development and career growth within the company
Meritocratic and inclusive environment