LinkedInNiraj Bhandari

Niraj Bhandari

Product Manager and data science enthusiast

Location
Bengaluru Area, India
Industry
Management Consulting
Current
  1. McKinsey & Company,
  2. JSChannel
Previous
  1. Yahoo!,
  2. Covansys,
  3. Infogain
Education
  1. IIM Calcutta
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500+connections

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500+connections
McKinsey & Company

McKinsey & Company

Product Manager

– Present

View full profile

Background

Summary

A seasoned professional with 11+ years of experience building products.

The portfolio of work includes
* Enterprise Social Platform
* Data Science
* Mobility Strategies
* eCommerce platforms
* Real time mission critical Aerospace systems.
* Navigational Database system for Aerospace.
* Decision Support System for marine terminals.

Areas of Interest
* Data Science
* Marketing Analytics

Certifications include
* CSP - Certified Scrum Professional
* CSPO - Certified Scrum Product Owner
* PMP
* Six Sigma Green Belt

Specialties:
* Agile Methodologies
* Product Management
* Data Analytics
* Team Building
* eCommerce
* SaaS
* Internet Technologies

Experience

Product Manager

McKinsey & Company
– Present (2 years 6 months)

Organizer

JSChannel
– Present (less than a year)www.jschannel.com

JSChannel is not for profit community for & by Javascript Enthusiasts.

Yahoo! Small Business

Yahoo!
(3 years)Bengaluru Area, India

* Team building.
* Hiring and mentoring team.
* Prioritizing and Planning product releases.
* Building scalable systems
* Yahoo! Small Business Platform
* Customer Acquisition funnel.
* Product Life Cycle, ordering and subscriptions.
* Yahoo! Store

Team Leader, PayPal IDC

Covansys
(7 months)Chennai Area, India

* PayPal’s Youth Debit Card
* Mentoring team.

Senior Software Engineer

Infogain
(1 year 1 month)Noida Area, India

* Decision support system for marine terminals.
* Estimation Model - Conference Paper
* Mentoring and getting new members up to speed.

Senior Engineer

Honeywell
(4 years 3 months)Bengaluru Area, India

* Mission Critical real time systems
* New navigational database system
* Transitioning ownership of one of the product lines to India
* Team building and mentoring

Skills

  • Service Delivery
  • Product Management
  • Agile Methodologies
  • E-commerce
  • Team Building
  • SaaS
  • Program Management
  • Integration
  • Cross-functional Team...
  • PMP
  • Leadership
  • Tableau
  • MapReduce
  • Data Science
  • SQL
  • Python
  • NoSQL
  • Hadoop
  • Project Planning
  • Process Improvement
  • Scalability
  • Six Sigma
  • C/C++
  • Agile Project Management
  • Competitive Analysis
  • See 10+  See less

Certifications

Six Sigma Plus Green Belt

Honeywell
– Present

Six Sigma Green Belt

Covansys
– Present

Project Management Professional (PMP)

Project Management Institute
– Present

Certified Scrum Product Owner (CSPO)

Scrum Alliance
– Present

Certified Scrum Professional (CSP)

Scrum Alliance
– Present

Publications

Calibrated Estimation Model for a Maintenance Project(Link)

11th IASTED International Conference on Software Engineering and Applications
November 2007

Effort and size estimation for legacy products under maintenance phase is very challenging as most of legacy products were developed several years ago when software estimation techniques were not mature; these projects were often estimated using the rule of thumb. In this paper, we propose a simple yet effective model to estimate size and effort for maintenance of a legacy product based on Function Points, which are calculated from the Lines of Code (LOC) using a reverse engineering technique. Subsequently these Function Points are calibrated in order to accommodate factors such as product/domain knowledge and learning curve characteristics. The proposed model builds on the fact that the maintenance effort for a domain knowledge intensive project is substantially different than the maintenance effort for normal projects. It also takes into account the fact that descend on learning curve is steeper in the case of domain intensive projects. We validate outcome of the analytical model with measurements from a real-world maintenance project of a legacy product that heavily depends on domain knowledge and learning curve characteristics. The validation shows that the results from this analytical model and the real world data are in close synergy, which emphasizes the effectiveness of the model.

Authors:

Education

IIM Bangalore

General Management

BITS Pilani

MS, Software Systems

CDAC Bangalore

PG Diploma in Advanced Computing

Bangalore University

BE (Computer Science)

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