EduShine - Faculty - Big Data Science - IIT/IIM/MDI/ISI/IISc

Designation : Faculty - Big Data Science

Role: Teaching

The opening is is one of India's leading technology enabled education companies. They offer high quality, impactful and industry relevant learning programs to working professionals through convenient technology-enabled blended and online modes, enabling them to develop new competencies and achieve career growth without disrupting their career. Apart from providing India's #1 rated Analytics course (our Post-Grad Program in Business Analytics), we also offer a Big Data Analytics program and a rapidly growing Exec MBA, in addition to specialized courses such as our Financial Risk Analytics program.

The team are alumni of leading institutions like Stanford, Berkeley, MIT, Kellogg, IIT Bombay, IIT Madras, Duke, MDI, IIM and have previously worked in companies such as Tiger Global, Flipkart, Snapdeal, Boston Consulting Group, Zipdial, SAP, Stratify, etc.

Desired Profile:

PhD in Data Sciences / Mathematics / Analytics / Computer Science / Economics from IITs, IIMs, IISc, ISI etc or globally ranked foreign institutions.

We are looking for candidates who have significant teaching/training experience at the intersection of Big Data technologies and machine learning on the Big Data environment. The individual must be comfortable with programming, with a strong preference for knowledge of Python. Experience working with and teaching Hadoop and Spark is essential.

You- re a good fit if you:

- Have experience using Hadoop and/or Spark in analytics (modelling and implementation of models)

- Are comfortable wrangling large, complex datasets

- Have demonstrated experience building, validating and tuning models

- Are proficient in Python (experience with PySpark desirable)

- Have taught in a classroom setting, preferably with working professionals

You will be joining an accomplished faculty body at - a blend of academics with decades of experience, and industry practitioners and experts. You will teach classroom sessions (physical, on-premise). All classes are held on weekends.