Senior Data Scientist

Cint

. Architect statistical methodologies and machine learning codebases powering Media Measurement products.

Posted 10/9/2026full-timeRemote • BrazilSeniorWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in statistical methodologies and machine learning model development, with a strong ability to analyze and interpret large datasets. Proficient in Python and SQL, capable of delivering insights and visualizations to both technical and non-technical stakeholders.

Highest-signal resume keywords

Statistical MethodologiesMachine Learning TechniquesData Analysis and InterpretationPython for Statistical AnalysisSQL Query Optimization

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Statistical ModelingPredictive ModelingRegression AnalysisClustering TechniquesExperimental DesignHypothesis TestingData ValidationSampling TheoryStochastic ModelingData Manipulation

Soft Skills

Analytical SkillsCommunication SkillsSelf-StarterResearch Skills

Tools & Technologies

PythonSQLSpark

Certifications & Qualifications

Master's Degree in StatisticsMaster's Degree in Data Science

Industry Keywords

Media MeasurementMarket ResearchAdvertising AnalyticsDigital AttributionOnline Survey MethodologiesMultivariate Testing

Tech Stack

Tools & technologies

PythonSparkSQL

About the role

Key responsibilities & impact

Architect statistical methodologies and machine learning codebases powering Media Measurement products.
Lead research, discovery, and end-to-end development for new and existing machine learning and statistical models related to media measurement.
Extract, manipulate, and analyze massive and complex datasets to uncover insights that shape product strategy and roadmap.
Plan, develop, and maintain data science projects end to end with minimal supervision.
Partner with engineering and product teams on technical design, deployment, implementation, and ongoing validation of scalable machine learning models.
Develop, validate, and maintain statistical and machine learning models.
Conduct ongoing evaluation and validation of internal and external product methodologies.
Transform statistical outputs into visualizations and presentations for technical and non-technical stakeholders.
Communicate findings and statistical recommendations clearly to diverse business and technical audiences.

Requirements

What you’ll need

Master degree or equivalent in Statistics, Quantitative Sciences, Data Science, Operations Research, or another quantitative field.
5 years of experience in a data science capacity, preferably in market research or advertising analytics.
Ability to manipulate, analyze, and interpret large data sources independently.
Deep understanding of advanced statistical techniques and concepts, including properties of distributions, hypothesis testing, parametric and non-parametric tests, survey design, sampling theory, experimental design, regression or predictive modeling, and stochastic modeling or simulation.
Strong knowledge of machine learning techniques, including clustering, regression and their real-world trade-offs, and tree-based models and their real-world trade-offs.
Working knowledge in the application of statistical and modeling techniques.
Strong analytical skills with a focus on data validation and accuracy.
Ability to independently research and learn new methods, tools, and techniques.
Self-starter capable of carrying out projects end to end with minimal supervision.
Proficiency in Python for statistical analysis and machine learning model implementation.
Experience in media measurement and digital attribution (nice to have).
Experience with multivariate testing (nice to have).
Experience with predictive modeling (nice to have).
Experience modeling and quantifying outcomes by linking attitudinal survey data to behavioral purchase data (nice to have).
Experience in online survey methodologies (nice to have).
Ability to write and optimize SQL queries (nice to have).
Experience working with big data technologies such as Spark (nice to have).