Profile 1: Ram Narasimhan at GE

In What Data Scientists Do All Day at Work, the interview between Wall Street Journal’s Deborah Gage and GE’s Ram Narasimhan bares it all. Although Mr. Narasimhan left an airline business to join GE in the Bay Area, his background in industrial engineering and PhD work, coupled with extensive experience managing airlines assets positioned him as an expert data solver and predictor at GE. He tells Deborah Gage that his job in GE as Data Scientist is quite different from an average Data Science job and that he spends most of his time maintaining assets in GE plants.

Narasimhan’s data-driven, predictive models help GE to plan, prepare, and manage their assets. Narasimhan also mentions that a big part of his job is taking phone calls, attending meetings and seminars, and keeping up with courseware.

Profile 2: Dan Mallinger at Think Big’s Data Science Practice

This career spotlight conducted by Life hacker brings forth the typical day of a Data Scientist in Silicon Valley. The common buzz around that town is that the thriving Data Science community in the Valley connects the identified data patterns to business decisions. Mallinger, with two degrees in mathematical sciences and organizational psychology, has solid academic training in computer science. Having spent years with business statistics and analytics, he finds himself uniquely positioned to head the Think Big team. Throughout his career, he delivered data solutions with open source technologies, but long before the term “Data Scientist” came into existence. Mallinger feels his professional role of analyzing and delivering real-world solutions has remained more or less same over the years. Mallinger also feels that his background in Social Sciences helped him to acquire good team building skills.

Mallinger describes the average Data Scientist’s work week as follows:

1. Typical work weeks devour around 60 hours.
2. The Data Scientists generally maintain internal records of daily results.
3. The Data Scientists also keep extensive notes on their modeling projects for repeatable processes.
4. The good Data Scientists can begin their career with a $80k salary, and the high-end experts can hope to make $400K.
5. The industry attrition rate for DS is high as organizations frequently lack a plan or visions for utilizing these professionals.

The Rewards of Being a Data Scientist

The most common feedback that was gathered after sampling practicing Data Scientists was that when an algorithm actually solves a real-world business problem, the feeling of pride and satisfaction that comes with it is the greatest reward for the professional.

As the automation process of data tasks such data cleansing, Data Governance, and data compliance continues to evolve, the future hope is that Data Scientists will be left to focus more on unraveling patterns and proposing effective data solutions.   A growing misconception is that advanced Machine Learning can replace the Data Scientists one day.

src: dataversity.net