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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 drive innovation 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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Employee, Mu Sigma

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