Creating Decision Sciences talent is not a one-time
effort, but a continuous process. This talent needs to be groomed and nurtured
in an environment where art and science can exist in harmony
Building an analytically strong organization that is focused
on helping people make better, faster, data-driven decisions, wouldn’t have
been possible without holistic view of Decision Sciences.
The future will witness the notion of analytics evolving
into Decision Sciences encompassing Math + Business + Technology + Behavioral
Sciences.
To achieve our vision of building the world’s largest
Decision Sciences organization, we are focused on creating Decision Sciences
soldiers, captains and generals.
Decision Sciences Soldiers are analysts who work
on solving business problems, generating and then communicating findings and
insights. They will come from varied backgrounds including Engineering,
Computer Science, Economics, Math, Statistics, Business and other
quantitatively oriented fields. They will need to develop skills in analytics
techniques, data, technologies and applications along with a combination of
consultative first-principles-based thinking for problem definition and
hypothesis-based approaches. Also they will need to bring together right- and
left-brained thinking to balance the rigor of science with the creativity that
business requires.
Decision Sciences Captains are quantitatively
oriented professionals, who can lead teams of analysts from varied backgrounds
and solve business problems for our customers across multiple industry
verticals and functional roles such as marketing, risk and supply chain. Agile
and iterative project management skills will be essential to adapt to the
dynamism of the business problem environment and to manage new processes that
cut across functional boundaries. They will also need to develop knowledge
management frameworks, leveraging insights from across verticals and domains to
driveinnovation in
addition to effectiveness and efficiency. These captains will play a pivotal
role in consumption of analytics. They will simultaneously play the role of
explaining the science behind the analytics to the business user; ultimately
translating findings into insights and recommendations.
We look for the following key traits in every Mu Sigma team
member:
Necessary Traits
|
Description
|
Learning Over Knowing
|
Ability to apply first principles and structured
approaches to problem solving as opposed to relying excessively on past
domain expertise
|
Agility
|
Required to cope up with continuous transformation
|
Scale & Convergence
|
Potential to synergize in an ecosystem of talent,
capabilities, processes, customers and partners, which can be leveraged
across verticals, domains and geographies
|
Multi-disciplinary talent
|
Ability to apply Math + Business + Technology + Behavioral
Sciences
|
Innovation
|
Increase breadth and depth of problem solving by
constantly researching and deploying emerging techniques, technologies and
applications
|
Cost Effectiveness
|
Ensure sustainability and institutionalization of problem
solving across organizations
|
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