Data Scientist

Role overview

Join Cint as a Data Scientist working with Data Science and Analytics teams. You’ll collaborate with product and engineering to develop and validate Media Measurement and related Data Solutions products.

Key missions

• Contribute to discovery and development of new products/models for media measurement.
• Develop, validate, and maintain models by analyzing large databases to identify trends.
• Work with cross-functional teams to achieve project goals by communicating insights and recommendations.

Responsibilities

• Apply statistical and machine learning techniques to support measurement/data solutions.
• Perform data and model validation to ensure accuracy.
• Conduct independent data manipulation, analysis, and interpretation for large datasets.
• Implement ML methods and support model lifecycle (development → validation → maintenance).

Requirements

• Practical experience applying statistical and modeling techniques.
• Strong analytical skills focused on data/model validation and accuracy.
• Familiarity with core statistics, including:
  • distribution properties
  • hypothesis testing (parametric/non-parametric)
  • survey design, sampling theory
  • experimental design
  • regression/predictive modeling, classification
  • stochastic modeling/simulation
• Ability to learn new methods, tools, and techniques.
• 2+ years in data science/analytics, preferably in market research or advertising analytics.
• Strong capability to manipulate/analyze/interpret large data sources independently.
• Master’s degree (or equivalent) in Statistics, Quantitative Sciences, Data Science, Operations Research, or a related quantitative field.
• Ability to complete tasks independently while collaborating on broader project direction.
• Exposure to machine learning methods (e.g., clustering, regression, tree-based models) and trade-offs.

Nice to have / additional skills

• Experience in media measurement and digital attribution.
• Experience with multivariate testing.
• Experience with online survey methodologies.
• Python proficiency for statistical analysis and ML implementation.
• Ability to write and optimize SQL queries.
• Experience with big data technologies such as Spark.