Previously, full-stack Data Science and Data Engineering industry professional developing, architecting, and commercializing complex Analytical, Streaming, and Machine Learning workload for Big Data over Cloud and on-premises. Lead developmental efforts in creation and maintenance of scalable, resilient, fault-tolerant, and distributed architectures, tools, and products integrated over Analytics to unveil deep insights from the enterprise data.

Full Stack Data Sciences and ML. Data Engineering [Hadoop Stack, Apache Big Data Stack, Spark Stack, Elastic Stack, Enterprise SQL, NoSQL, and Graph Databases] Cloud Stack [AWS, Azure, and Google Cloud] Analytics [Statistical, Business, and Predictive Modelling, Deep Learning, and Machine Learning] Visualisation [Tableau, PowerBI, Qlik, D3, Open Source Solutions]

Accessing my fits for your open position, find below top three reasons to consider me:

1) Self motivated full-stack professional with high standards of professionalism, work-ethics, and startup mindset. All three of my previous positions were either in a new company or new teams. 2) My technical reporting heads describe me with several synonyms - someone, was awed by my speed and agility, and called me the elephant in the room on architecture design meetings, while others acknowledged me as a vociferous implementer and hard-working delivery engine. 3) With backgrounds deep in software and data engineering, and exposure across all the major global markets and top industries, I see myself as a full-stack Data Scientist. The one who can, not just do data wrangling and smart visualization or feature extraction or Algorithmic design, but also as one who can encapsulate big data (production pipeline) and software engineering (converting AI solutions into end-user framework or library or GUI tool).