Lead Data Scientist - Statistical Expertise

Role Overview: You will be researching and developing causal inference models to accurately measure the causal effects of advertising, promotion, distribution, assortment, and other factors. Your work will inform clients about the historical effectiveness of their marketing tactics and exogenous factors on outcomes. Additionally, you will be developing automated and scalable supervised and unsupervised learning methods to improve operational efficiency of causal inference solutions for accurate outcome predictions and labeling. Key Responsibilities: - Research, design, develop, implement, and test econometric, statistical, causal inference, and machine learning models. - Create various prototypes for research and development purposes. - Design, write, and test modules for Nielsen analytics platforms using Python, SQL, Spark, and AWS products. - Utilize advanced computational/statistics libraries including Spark MLlib, Scikit-learn, SciPy, StatsModel, SAS, and R. - Document and present econometric, statistical, and causal inference methods to within company associates. - Work with Product on setting the direction for new analytics research and product development for engaged track(s). - Partner with Product organization on resolving issues that users face. - Provide guidance to junior data scientists. Qualification Required: - PhD level coursework in Statistics, Econometrics, Applied Mathematics, Biostatistics, Chemometrics, Computer Science subfield of Machine Learning, Industrial Engineering, Operations Research, Psychometrics, Physics, or other relevant discipline. - Expertise in coding and testing of analytical modules using Python, SQL, Spark, and AWS. - Proficiency in at least one statistical software or machine learning package, such as Scikit-learn and StatsModels. - Experience with Git or other version control tools. - Well-organized and capable of handling multiple mission-critical projects simultaneously while meeting deadlines. - Excellent oral and written communication skills. - Systematic thinking skills and critical thinking skills. - Ability to solve problems independently and within a team. - Experience managing or mentoring data scientists. Please be aware that job-seekers may be at risk of targeting by scammers seeking personal data or money. Nielsen recruiters will only contact you through official job boards, LinkedIn, or email with a nielsen.com domain. Be cautious of any outreach claiming to be from Nielsen via other messaging platforms or personal email addresses. Always verify that email communications come from an @nielsen.com address. If you're unsure about the authenticity of a job offer or communication, please contact Nielsen directly through our official website or verified social media channels. Role Overview: You will be researching and developing causal inference models to accurately measure the causal effects of advertising, promotion, distribution, assortment, and other factors. Your work will inform clients about the historical effectiveness of their marketing tactics and exogenous factors on outcomes. Additionally, you will be developing automated and scalable supervised and unsupervised learning methods to improve operational efficiency of causal inference solutions for accurate outcome predictions and labeling. Key Responsibilities: - Research, design, develop, implement, and test econometric, statistical, causal inference, and machine learning models. - Create various prototypes for research and development purposes. - Design, write, and test modules for Nielsen analytics platforms using Python, SQL, Spark, and AWS products. - Utilize advanced computational/statistics libraries including Spark MLlib, Scikit-learn, SciPy, StatsModel, SAS, and R. - Document and present econometric, statistical, and causal inference methods to within company associates. - Work with Product on setting the direction for new analytics research and product development for engaged track(s). - Partner with Product organization on resolving issues that users face. - Provide guidance to junior data scientists. Qualification Required: - PhD level coursework in Statistics, Econometrics, Applied Mathematics, Biostatistics, Chemometrics, Computer Science subfield of Machine Learning, Industrial Engineering, Operations Research, Psychometrics, Physics, or other relevant discipline. - Expertise in coding and testing of analytical modules using Python, SQL, Spark, and AWS. - Proficiency in at least one statistical software or machine learning package, such as Scikit-learn and StatsModels. - Experience with Git or other version control tools. - Well-organized and capable of handling multiple mission-critical projects simultaneously while meeting deadlines. - Excellent oral and written communication skills. - Systematic thinking skills and critical thinking skills. - Ability to solve problems independently and within a team. - Experience managing or mentoring data scientists. Please be aware that j