Sayuj
Bakshi

As a decision scientist, I enabled my clients to make informed decisions by furnishing them with actionable insights and arming them with superior quality storytelling tools. I was responsible for aiding my client in any way required to solve an analytical problem, from number crunching to creating visualizations to employing machine learning algorithms.

Client: Fortune 500 Medical Device Manufacturer based in the USA

Developed 8 Tableau tools to visualize available data and make it amenable to analysis. The tools captured multi-faceted aspects such as sales, new product introductions, price monitoring, portfolio pricing analysis, elasticity, P&L for the medical devices and provided a holistic view of performance.

This easily understandable visual representation of data enabled all stakeholders to draw insights like never before. The stakeholders included all ranks in the organization from the steering committee and finance heads to the sales team.

Client: Australian Insurance Provider and Underwriter with revenue of 2.4 Billion USD

Conducted data quality assessment of provided customer data and identified key metrics for conducting analyses.

Conducted various analyses to affirm and disprove hypotheses about factors affecting customer churn and understood customer behavior. Thereafter, employed machine learning algorithms to identify customers at high risk of churning out.

Selected to collate and use internal domain knowledge and business use cases within Mu Sigma's ecosystem to generalize the problem solving process in order to optimize knowledge transfer and problem solving capabilities within the ecosystem.

Learning modules containing the collated learning were made in the form of videos using Camtasia software. In addition, modular code blocks were made which could be used to perform general operations, irrespective of the problem space.

This abstraction process was aimed at augmenting knowledge transfer for new recruits as well as to supplement capabilities within the organization.

CGPA : 2.51/4.0

Stream : PCM

Overall Score : 85%