Job Description
Cint, a pioneer in research technology (ResTech), offers a programmatic marketplace of nearly 300 million respondents worldwide, enabling businesses to build strategies, publish research, and accurately measure digital advertising impact.
Opportunity
As a Data Scientist at Cint you will work with Data Science, Analytics, product and engineering teams on Media Measurement and Data Solutions products. You will design statistical and machine learning methods, build and maintain models, and validate them to align Cint’s capabilities with market needs.
What you will do
- Contribute to discovery and development phases for new and existing media measurement products/models.
- Participate in model development, validation, and maintenance.
- Analyze large datasets to identify trends, patterns, and insights, ensuring quality and reliability of results.
- Respond to ad hoc client‑specific requests, performing analyses, data manipulation, and producing summary results.
- Collaborate with cross‑functional teams to achieve broader project goals.
- Develop methodologies, validate models, and enhance existing statistical and machine learning models.
- Support the evaluation and validation of both internal and external products to ensure Cint’s success.
- Communicate insights and recommendations through visualizations and presentations that resonate with a wide range of audiences.
Qualifications
- Master’s degree or equivalent in Statistics, Quantitative Sciences, Data Science, Operations Research, or other quantitative fields.
- At least 2 years of experience in a data science or analytics role, preferably in market research or advertising analytics.
- Ability to manipulate, analyze, and interpret large data sources independently.
- Familiarity with core statistical concepts and techniques (e.g., distributions, hypothesis testing, survey & sampling design, experimental design, regression, classification, stochastic modeling).
- Exposure to a variety of machine learning methods (clustering, regression, tree‑based models, etc.) and their real‑world advantages and drawbacks.
- Practical experience applying statistical and modeling techniques.
- Strong analytical skills with a focus on data and model validation and accuracy.
- Comfortable with learning new methods, tools, and techniques.
- Ability to complete assigned tasks independently while collaborating on overall project direction.
- Proficiency in Python, particularly for statistical analysis and implementing machine‑learning models.
Bonus Points If You Have
- Experience in media measurement and digital attribution.
- Experience in multivariate testing.
- Experience in online survey methodologies.
- Ability to write and optimize SQL queries.
- Experience working with big data technologies (e.g., Spark).
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