Verified recordTelecommunications

Arnab Dhar

General Manager at Ericsson

Based in Budge Budge-I, India

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Seniority

Manager

Department

General Business & Management

Location

Budge Budge-I

Industry

Telecommunications

Company size

107K

Contact information

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Email

1 credit

a•••••••@ericsson.com

Phone

5 credits

+91 ••• •••• ••••

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Background

About Arnab Dhar

IBM India 1994– 1998 Started career as ABAP developer and worked in multiple domestic projects. Worked as Lead in multiple domestic projects, and then leveraged experience in international projects. Worked as Deliver Manager in multiple projects @UK, and played global delivery manager in multi-country SAP rollouts 2006–2008 Worked as Technical Solution Manager in multiple assignments in the development of proposal response strategies and themes, pricing approaches. 2008–2009 Worked as Large Deal Technical Solution Manager in several pre-sales workshops, due diligence. Played the primary source of global delivery engagement management and solution design across technical disciplines. 2009– 2011 Worked as Global Delivery Service Manager in managing the application services (both deployment and sustain services) for the largest nutritious and food company. Played a pivotal role in the design of harmonized delivery model across different rollouts in US, Europe, and Asia Pac. Also, managed multi-country SAP implementation projects for a German Chemical and Pharmaceutical company with the ultimate aim of facilitating business performance improvements.@ Ericsson India 2011– 2014 Worked as Account Manager and established IT services with 250+ practitioners 2015– 2016 Worked as ADM Competency Lead to enhance core competencies – Microsoft, Java and Open Source, Enterprise Application Integration, Testing, and Application Platform services 2017– Till Date Worked as Strategy Execution Manager in establishing an ecosystem of start-ups to develop innovative AI solutions. In addition to this role, developed machine learning algorithm using multivariate logistic regression to classify the data with respect to different performance degradation issues in network at a future date (in a 5 days forecasting window). Also, developed deep learning algorithm using recurrent neural network to simulate the network performance.

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