Responsibilities

• Use various statistical techniques like but not limited to predictive modeling, customer profiling, segmentation, data mining etc. to provide relevant & accurate insights.
• Execute predictive model scorings in order to support various customer marketing initiatives.
• Query structured & unstructured data (from hybrid sources & platforms) and perform exploratory data analysis for further advanced modeling.
• Present ideas and findings with actionable recommendations in an easily consumable manner.
• Participate in peer reviews of projects.
• Stay current with business results, strategies, industry standards and best practices.

Qualifications

• M.S. in Mathematics, Advance statistics, physics, operations research, bio statistics &/or computer science with 2+ years of experience in statistical/quantitative analysis.

Skills/Experience

• 2+ years of experience in customer / marketing analytics role preferably in retail domain
• Expert level proficiency in SAS (Base SAS, Enterprise Guide, Enterprise Miner), advanced SQL, R, Python.
• Experience in working with large relational databases (Teradata, Oracle).
• In-depth knowledge of statistical procedures that are applied in segmentation, profiling, and data mining. Prior experience in machine learning techniques will be preferred.
• Experience in Excel and PowerPoint. Ability to automate insights / reports using Macros (SAS and Excel VBA) & other relevant technologies.
• Ability to understand business needs to generate relevant accurate insights or translate business needs into analytical specifications.
• Demonstrated analytical & problem-solving skills, ability to interpret reports, analyze trends and provide insights.
• Strong written and verbal communication skills. Demonstrated ability to communicate complicated statistical analytical concepts to business stakeholders in a simplified comprehendible manner.
• Highly energized personality with a positive attitude and ability to work with minimal supervision, prioritize, multitask and work under tight timelines.