Senior Data Scientist

About this role

As a Senior Data Scientist , you will sit at the intersection of product, engineering, and advanced analytics. You will architect the statistical methodologies and machine learning codebases that power our Media Measurement products, directly translating raw global data into strategic insights that drive the ResTech industry forward. What You Will Deliver: Lead the research, discovery, and end to end development phases for new and existing machine learning models relating to media measurement.

Extract, manipulate, and analyze massive datasets to uncover insights that shape and direct our product roadmap. Partner closely with engineering and product teams to align on the technical design, deployment, and ongoing validation of scalable machine learning models. Transform complex statistical outputs into compelling visualizations and presentations that resonate with diverse business and technical audiences. Key Responsibilities: Lead research, discovery, and development phases for new and existing statistical models relating to media measurement.

Plan, develop, and maintain data science projects end to end with minimal supervision. Analyze large, complex datasets to extract insights that inform product strategy. Collaborate with cross-functional engineering and product teams on model design and implementation. Develop, validate, and maintain statistical and machine learning models. Conduct ongoing evaluation and validation of internal and external product methodologies.

Communicate findings and statistical recommendations through clear visualizations and presentations for technical and non-technical stakeholders. 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 Stochastic Modeling or Simulation. Strong knowledge of machine learning techniques, such as: Clustering Regression and their real-world trade-offs Tree-based models and their real-world trade-offs Working knowledge in the application of statistical and modeling techniques.