Details:

Position Summary:

We are looking for Consultant with hands on experience in predictive analytics and SAS to be based out of our San Francisco office.

Key Responsibilities:

• Being a part of the team working on marketing analytics / CRM projects with global clients.
• Applying analytical tools and techniques and effectively communicating the results to various internal/external stakeholders.
• Understand the nature, structure & quality of data, identify data issues and suggest solutions.
• Execute planned analysis to meet the project objectives.
• Ensure on-time & error free delivery, proactively escalate issues when required.
• Technical
• Data Management & Manipulation
• Exploratory Analysis & Making Presentations
• Using Tools like basic Excel, SAS, R, SPSS etc
• Statistical Analysis & Design
• Quality/Process Compliance & Improvement Initiatives
• Having a complete understanding & awareness of the various internal processes, ensuring compliance to the same and suggesting process improvement initiatives
• Client Servicing
• Executing tasks around client servicing like proactively responding to client requests, seeking feedback etc
• Observing seniors and understanding how to deal with client requests effectively.
• Communication
• Effectively communicating with all the internal and external stakeholders, keeping them updated on technical findings, issues or any other required project related development

Qualifications and Skills:

• 48 Months (Minimum) experience in a quantitative role.
• Experience in the area of contributing to Analysis by building Logistic Regression Models / Segmentation Skills
• Bachelors degree (or equivalent) in a Numerical Course e.g. Statistics, Mathematics, Physics, Economics, Engineering, Computer Science, IT or equivalent
• Masters qualification in statistics / engineering or similar (desired)
• Strong statistical knowledge, SAS skills (data manipulation, macros, statistical procedures, etc.)

Min. Qualification:

ug

Skills Required:

Data analytics, excel, logistic regression, r, sas, spss, statistical techniques