Principal Supervisor - Data Science

Description

• Oversees team of analysts and data scientists in the application of data science methods to identify business opportunities, predict performance outcomes and provide tactical and strategic recommendations
• Discovers insights from Big Data to help shape or meet specific business needs and goals
• Ensures the accomplishment of the following core supervisory/management functions for a given group(s) within a business unit: planning, organizing, directing the performance of on-going activities and/or special/ad hoc assignments, staffing (employee selections, training, coaching and performance management, time entry), coordinating, reporting and budgeting
• Implements new, industry-leading statistical, mathematical, machine learning or other methodologies for modeling or analyses
• Supports scientific, measurable, and fact-driven approaches by adding statistical rigor to business recommendations
• Translates business and analytics strategies into multiple short-term and long-term projects, and ensures end-to-end execution of projects
• Leads the continuous improvement commitment and efforts for team, including designing processes, establishing quality and quantity standards and metrics, collecting, refining, adapting, and communicating best practices, sharing knowledge, and developing staff in a systematic fashion; uses process design outcomes to solve problems
• Identifies and evaluates technologies and provides strategic input to advance the organization’s analytics capabilities
• Utilizes business expertise to translate goals into data-based deliverables, such as predictive models, pattern detection analysis or optimization algorithms and methods

Requirements

• Intermediate- to advanced-level knowledge and skills in data modeling, data structure, and the application of complex SQL queries with data from multiple sources, including a Big Data platform (e.g., Hadoop, AWS, Azure)
• Intermediate- to advanced-level skills and experience in articulating business questions, pulling data from relational databases (e.g., SAP Business Warehouse (BW), ORACLE, SQL SERVER) and using advanced excel and statistical tools (e.g., Minitab, Alteryx, Advanced Excel with VBA, R, SAS, SPSS, Stata, MATLAB, etc.), and determining the appropriate analytical approach to conduct in-depth analysis to support decision making
• Bachelor’s degree in quantitative field (e.g., Statistics, Computer Science, Information System, Engineering, Mathematics, Physics, Data Science, Industrial/Organizational Psychology and Econometrics, etc.) or Business Administration, and 7 years of experience working in a data analytical, computer programming, or engineering function, inclusive of two years of experience leading analytical projects; OR
• Proficient in programming skills such as SQL, C/C++/C#, Java, R, Python, PHP, or SAS
• Experience in quantitative analysis, query design, data visualization, statistical analysis, and predictive analytics
• Master’s degree in quantitative field (e.g., Statistics, Computer Science, Information System, Engineering, Mathematics, Physics, Data Science, Industrial/Organizational Psychology and Econometrics, etc.) or Business Administration, and 5 years of experience working in a data analytical, computer programming, or engineering function, inclusive of two years of experience leading analytical projects
• Adept with multiple business intelligence tools and platforms (e.g., SAP Business Objects, Microsoft Power BI, Microsoft Azure, AI, and machine learning tools)
• Ability to develop models in a Big Data cloud environment and deploy models in production
• Proficient in quantitative analytics (e.g., data mining, regression analysis, hypothesis testing, A/B testing, machine learning modeling, including multivariate statistical analysis, natural language processing, unsupervised and supervised learning, deep learning, predictive modeling, and model optimization in production)
• Intermediate level skills and experience with data mining and statistical analysis using analytical packages / tools (e.g., R, SAS, SPSS, Stata, MATLAB, Minitab, etc.)
• Ability to lead analytical data projects across functional teams, including coaching and feedback of other team members on analytic approaches
• Excellent communication skills, including ability to interact with and influence decision-making by non-analytical business audiences

Benefits

Training + Development

Interview process

Visa Sponsorship

Security clearance