Über
The Opportunity As a Data Scientist at Cint you will have the opportunity to work alongside our Data Science and Analytics teams and collaborate with product and engineering teams to work on Media Measurement and Data Solutions products. This includes data analysis, design of statistical and machine learning model methodologies and codebases, and model validation. The ideal candidate is a self-starter comfortable working with large datasets and knowledgeable in statistical and machine learning techniques, with an eagerness to learn and contribute to the development and validation of products that align Cint capabilities with the market. Responsibilities
Contribute to discovery and development phases for new and existing products/models relating to media measurement 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 including performing analyses, data manipulation and producing summary results. Collaborate with cross-functional teams to deliver on broader project goals. Participate in developing methodologies, model validation, and maintenance and enhancement of existing statistical and machine learning models. Support evaluation and validation of both internal and external products to ensure Cint’s success. Communicate insights and recommendations through visualizations and presentations that will 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. 2 years of experience in a data science and analytics capacity, 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. properties of distributions, hypothesis testing, parametric/non-parametric tests, survey design, sampling theory, experimental design, regression/predictive modeling, classification, stochastic modeling/simulation, and more). Exposure to a variety of machine learning methods (clustering, regression, tree-based models, etc.) and their real-world advantages/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. Able to complete assigned tasks independently while collaborating on overall project direction and broader project goals Proficiency in Python (as it relates to statistical analysis and implementing Machine Learning models) Bonus Points
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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Sprachkenntnisse
- English
Hinweis für Nutzer
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