Senior Data Scientist

Ambev

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

Posted 9/16/2026full-timeRemote • BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in developing and enhancing prediction models, utilizing Python and PySpark for data processing in cloud environments. Proficient in recommendation techniques and A/B testing to drive business decisions and optimize customer experiences.

Highest-signal resume keywords

Python ProgrammingPySpark Data ProcessingMachine Learning Model DevelopmentA/B Testing and Causal InferenceRecommendation Techniques

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data AnalysisMachine LearningStatistical FoundationsMarket Basket AnalysisClustering TechniquesCustomer SegmentationModel ValidationRecommendation SystemsIncremental Impact MeasurementFeature Engineering

Soft Skills

Strong Communication SkillsAbility to Generate Actionable Insights

Tools & Technologies

AWSAzureData PipelinesFeature Stores

Certifications & Qualifications

Bachelor's Degree in Data Science or Related FieldGraduate Degree in Data Science (Preferred)

Industry Keywords

Consumer DataPersonalizationRevenue OptimizationCustomer Behavior AnalysisRecommendation Architectures

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 prediction models for Zé Delivery.
Create and maintain data pipelines and features in PySpark to support models at scale.
Develop product and offer recommendation strategies throughout the customer journey.
Define, execute, and analyze A/B tests to assess the impact of models and campaigns.

Requirements

What you’ll need

Bachelor's degree or equivalent higher education qualification.
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).
Strong foundations in statistics, machine learning, and model validation.
Ability to translate analytical models into business decisions and impact on key metrics.
Preferred qualifications: experience with modern recommendation architectures, user and item embeddings, real-time and low-latency recommendation systems, feature stores, online/offline feature serving, multi-armed bandits, reinforcement learning applied to recommendation, a graduate degree in Data Science or a related field, strong communication skills, and the ability to generate actionable business insights.

Benefits

Comp & perks

Medical Insurance
Dental Insurance
Telemedicine
Life Insurance
Gympass
Discount 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 payment
Childcare or nanny allowance
Company profit sharing
Merit-based and inclusive environment
Development and career growth opportunities within the company