existence of such imperfect but constructive proposals for data science careers
was itself a huge, positive signal. Too often, business organizations view analytics people as high-priced commodities
to be acquired when clearly needed and
discharged casually when not. Knowing
this, the skilled professional is compelled
to make sure that their own financial and
intellectual needs are taken care of, even
when that means leaving the company
for better opportunities (and there are
typically many opportunities available to
skilled data scientists). In such cases, a
data scientist ends up leaving a relatively
good situation largely in order to feel appreciated, while the company finds that a
unique collection of broad analytics skills
and hard-earned domain knowledge has
just walked out the door.
Secondly, I was struck by just how
many different technical competencies
their proposed plan required, even for
people who wanted to pursue managerial and leadership roles in data science.
When we discussed this, they were adamant about the need for this broad and
deep set of capabilities, both in order to be
skilled in creating and assessing sources
and to be credible within the data scientist
community.
Not long after this discussion, a former
MBA student of mine came to visit me.
“Richie” had taken several courses with
A NA L Y T I C S
me and had landed an interesting job as
data analyst for a large global organization. After a year and a half on the job, he
turned down a good opportunity to move
into a line management position. Instead,
Richie had decided to go back to graduate
school again, this time to get a master’s
degree in analytics. “My company doesn’t
know how much it is leaving on the table,”
he told me, “but I do. I just need more
technical capabilities to be a real hero in
this kind of environment.” His five-year
goal, however, was a senior analytic leadership role, and both he and I were confident that he would get there, because of
the broad background from his MBA and
also because of his strong commitment to
learning and growing on all fronts.
When someone with that sort of attitude
gets enough technical chops, look out!
OK, I’m done pontificating for now.
More next time.
Vijay Mehrotra ([email protected]) is
an associate professor in the Department of
Analytics and Technology at the University of San
Francisco’s School of Management. He is also an
experienced analytics consultant and entrepreneur,
an angel investor in several successful analytics
companies and a longtime member of INFORMS.
NOTES & REFERENCES
1. Kristof, Nicholas D., “Professors, We Need
You,” New York Times, Feb. 15, 2014.
2. See http://www.predictiveanalyticsworld.com/
sanfrancisco/2014/agenda_overview.php for the
complete agenda for PAW 2014 SF.
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