Danish Haroon is a visiting faculty of Data Mining and Big Data at KSBL. He has over 6 years of experience in the data science space, spanning along the verticals of retail, banking, and utility.
He has worked in consulting roles for multiple offshore clients, and delivered data driven products within the local market as well. During his experience at K Electric, he has built a team from scratch, providing value to business units by building end-to-end machine learning pipelines.
He is an MBA from KSBL and did his MS Data Science from FAST NUCES. He is associated with a research group working on tackling with the issues in long tailed tabular data, and aspires to pursue his Ph.D. along the same lines.
Education
MS in Data Science, FAST NUCES, Pakistan
Learning a better representation while retaining classification performance on imbalanced data
2021
MBA, KSBL
Feasibility study for launching a water-displacing spray in the local market
2015
BS Telecommunication Engineering
2012
Consulting & Teaching
- K Electric
Manager – Analytics
2019-Present
- qordata
Data Scientist Consultant
2018-2019
- Market IQ Inc
Data Scientist
2016-2018
- PredictifyMe
Senior Data Analyst
2015-2016
- Ephlux
Idea Engineering Consultant
2014-2015
Details of Keynote Speeches, Panelist and Chair
- Guest Speaker session on “Open Archive – Innovating the meter reading process within utilities”, AI Hub, Inqline, 10 April 2021
- Guest Speaker session on “The World of Predictive Analytics”, Consulting webinar, qordata, 18 December 2021
- Guest Speaker session on “Release the Breaks & Accelerate Your Journey to AI”, DataCon Pakistan 2019, Marriot Karachi, 14 November 2019
- Guest Speaker session on “Machine Learning Modelling with ScikitLearn/GraphLab”, Pycon Pakistan 2019, Habib University, Karachi, 12 October 2019
- 8 week long workshop on “Data Science 101”, qordata, 10 October – 28 November, 2018
- Guest Speaker session on “What it takes to be a Data Scientist”, NED UET, Karachi, 4 August 2016
Publications & Research
- “Python Machine Learning Case Studies”, Apress 2017, ISBN # 9781484228227
- “Deep generative models to counter class imbalance: a guided model selection strategy”, 2021, IEEE Access, Vol 9, pp. 55879-55897