Lead Data Scientist

• 5 years of professional work experience in Statistics Data Science and/or related disciplines with focus on delivering analytics software solutions in a production environment
• Expertise with relational and distributed database systems with expertise in tools such as Spark Presto and other SQLbased querying engines
• Expertise in coding and testing of analytical modules using Python SQL and Spark.
• Expertise in at least one statistical software or machine learning package such as R or Scikitlearn
• Expertise with optimization techniques and tools such as AIMMS Gurobi etc
• Expertise with DevOps tools and CI/CD workflows including Git
• Expertise with sophisticated data mining forecasting & modeling solutions and the tools that support them such as Scikitlearn PyTorch and PyMC3
• Wellorganized clear communication and an ability to handle multiple competing priorities in a fastpaced environment
• Graduate/Post Graduate degree in Statistics Economics Applied Mathematics Computer Science Engineering or other Quantitative field of study
• Exceptional problem solving skills. Abilities to solve problems independently and within a crossfunctional team to resolve any hurdles in the projects
• Critical thinking skills to evaluate results in order to make timely decisions
• Mentor and provide guidance to other data scientists
• (Preferable) Experience working in cloudbased environments such as Azure AWS or GCP
• (Preferable) Experience with ETL frameworks like Airflow
• (Preferable) Domain expertise in survey sampling and/or audience measurement

• Research design develop implement and test econometric statistical optimization and machine learning models.
• Design write and test modules for analytics platforms using Python R SQL and/or Spark.
• Utilize advanced computational/statistics libraries including Spark MLlib Scikitlearn SciPy StatsModels or R.
• Collaborate with cross functional Data Science Product and Technology teams to integrate best practices from across the organization
• Provide leadership and guidance for the team in the of adoption of new tools and technologies to improve our core capabilities
• Execute and refine the roadmap to upgrade the modeling/forecasting/control functions of the team to improve upon the core service KPI s
• Gain a deep understanding of the Audio methodology supported by the team
• Ensure product quality stability and scalability by facilitating code reviews and driving best
• practices like modular code unit tests and incorporating CI/CD workflows
• Explain complex data science (e.g. modelrelated) concepts in simple terms to nontechnical internal and external audiences

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