The Life of a Data Scientist

pg_dslife_080916The worldwide demand for qualified Data Scientists continues to grow and one of the most common queries in many an industry insider’s mind is so, what is it that a Data Scientist does? When lay persons within an industry try to grasp the essence of a Data Scientist’s life, the confusion that boggles everyone is that this unique profession cannot be linked to one particular trade, one particular academic degree, or a single organizational role. Rather, the Data Scientist’s role is to identify and solve day-to-day business problems through a multi-disciplinary approach that encompasses many different skills like mathematics, statistics, computer science, operational research, and of course, business.

The most important thing to remember about a Data Scientist’s role is that just expert applications derived from statistics, computer science, and mathematics will not solve business problems. The Data Scientist, to begin, must appreciate and understand the domain-specific problems of a business and learn people tackle their daily processes. In a way, the Data Scientist’s job is to first understand the existing business processes, then identify the underlying problems, and then attempt reaching solutions through data-driven technologies to streamline the processes for better business gains. It is common to find Data Scientists arriving on the job from widely disparate academic backgrounds and experience levels—Machine Learning and programming, theoretical statistics and modeling, and also conventional mathematics fields. When these persons enter the Data Science field, they all display one common characteristic—a tremendous curiosity to unravel the mystery behind business data.

The most important qualifier that distinguishes Data Scientists from their professional peers is the superior ability to deliver algorithms that promise to solve business problems. In fact, the Data Scientist’s greatest motivation is to solve enterprise-level process problems through data-enabled systems and tools, which even the non-data experts can use in their daily business lives. The Data Scientist rejoices when his or her solutions help solve real world problems.

The second-most important aspect of a Data Scientist’s job is the “cross-functionality” of project execution. In Data Science, a large amount of work time must be invested in aligning business goals with technology goals. In a large enterprise with competing team agendas, the job of aligning teams across business groups, analytics teams, and technology experts can be a real challenge.

As Forbes tries to review the Data Scientist position as currently is use in the US, they use Glassdoor as a reliable source of career-rating services, to collect feedback about the Data Scientist position. In the 2016 Glassdoor report that rates careers based on salary structures, career advancement prospects, and professional status, Data Scientist occupies the top rank. The majority of Data Science employees surveyed by Glassdoor have responded that the appeal of delivering highly visible, real-world solutions is the biggest incentive for the data professionals. These professional have also commented that around 80% of their work time is spent on data cleansing and data preparation while only 20% is spent on executing solutions. In this context, one may want to review the article Diversity in Data Science Jobs.

An Umbel blog post tries to describe the Data Scientist’s life in terms of tasks performed at different times of the day. This post explains that in a typical work day, mornings are filled with meetings and networking to discuss problems and report progress. Post meetings, the teams could be spending time on data research, exploration of statistical procedures, or modeling. Afternoons can be dedicated to client consultation or business development activities. At the end of the day, the work teams generally meet to discuss the achievements of the day.

This Booz Allen newscast reveals that the an average Data Scientist spends most of the work time in understanding data, detecting patterns, developing algorithms, and writing programs to answer the queries that users have about the business data.  Many times, these different tasks cannot be fulfilled by a single scientist; so Data Science professionals are commonly seen to be working in team environments where distinct individuals bring distinct expertise to fulfill a common goal.

A Rutger’s University article titled A Day in the Life of a Data Scientist makes some interesting observations as follows:

  • The Data Scientist’s life can vary a great deal depending on the business needs and the actual working conditions.
  • The key skill required for success on the job is a keen understanding of the “data.”
  • As evolving Data Scientists will continually need to communicate best practices to their peers, they need to acquire superior communication skills.

To follow up on the above general round-ups of Data Scientist jobs, let’s look at three established job profiles:

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.

 Profile 3: John Hooks as Data Consultant

Job Shadow’s interview with John Hooks  provides a holistic view of what an expert consultant in the field of Data Science can offer to its clients. As a Council Member of a Technology and Media practice, Hooks offers his data solutions through Big Data Predictive Analytics.  Hooks also considers himself an entrepreneur with a “consistent track record of establishing ventures.”

Hooks describes the Data Scientist’s life to be a combination of systems maintenance work, development work, and Data Analytics works for eager clients. According to hooks, starting salaries for Data Science professionals can be anywhere between $60K and $110K, based on their academic backgrounds and professionals skills such as an MBA. He mentions that the Chief Data Scientist post at firms like Linkedin or Facebook fetch around $175K salary package. Read here to find out what DATAVERSITY® thinks of the Chief Data Scientist. Some more Data Scientist profiles are included in this McKinsey Report.

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.

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