Some InfoAbout Me

I am a Analyst (Data Engineer) at Goldman Sachs.

I am a keen learner with a successful track of designing and implementing the right-fit technology strategy and solutions to deliver under pressure. I am currently building big-data platforms to solve financial problems. I have competencies in the fields of Big-Data, Machine-Learning, Data-Science, Web-Development, and Mobile-Application-Development. I want to explore more about AI as I am fascinated by how AI is changing the world.

Resume

My WorksTechnology Stacks I worked On

Big Data

Machine Learning Modeling

Data Science

Deep Learning

Optical Charcter Recognition

Web Scraping

Game Application

Audio and Image processing

Web Development

Internet of Things

Linux

Programming Language

2000

Python

2000

Java

Strongest Area

1600

Data Structure and Algorithm

1600

Big Data

1200

Machine Learning

1200

Data science

My Education

B.Tech,Electrical Engineering

IIT Bhubaneswar,2016- 2020

Higher Secondary Education,Science(PCM)

Cotton College,2013-2015

High School

Damdama H.S. School,2000-2013

My Experience

Data Engineer

Analyst at Goldman Sachs, July,2020-Current

AI Engineer

Intern at PNB Metlife, May,2019-July,2019

Research Associate

Intern at IIT Guwahati, May,2018-July,2018

Automation Engineer

Intern at Vasitar Pvt Ltd, May,2017-July,2018

Public Relationship Manager

Intern at Cognitio Education , November,2016-January,2017

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coursesRelevant Courses and Certifications

  • learn about Introduction to the C programming language, Conditional statements, Loop constructs, Pointers and functions, Arrays and Strings ,Stacks , Structures and unions , Linkedlists , Queue , Stack memory and heap memory, Memory allocation and de-allocation , Trees , Graphs

    CS1L001:Data Structures and Algorithms

    Btech curiculum

  • Learn about the essential data structures like linked lists, stacks, queues, trees, and hash tables, and searching and sorting algorithms ,graph and string-processing algorithms along with the greedy as well as Dynamic Programming .

    Algorithms

    Coursera

  • Learn to use powerful, open-source, Python tools, including Pandas, Git and Matplotlib, to manipulate, analyze, and visualize complex datasets.

    Python for Data Science

    Edx

  • learn about (i) Supervised learning (parametric/non-parametric algorithms, support vector machines, kernels, neural networks). (ii) Unsupervised learning (clustering, dimensionality reduction, recommender systems, deep learning). (iii) Best practices in machine learning (bias/variance theory; innovation process in machine learning and AI).

    Machine Learning by Stanford University

    Coursera

  • learn about 1 - Data Preprocessing, 2 - Regression 3 - Classification 4 - Clustering 5 - Association Rule Learning 6 - Reinforcement Learning 7 - Natural Language Processing 8 - Deep Learning 9 - Dimensionality Reduction 10 - Model Selection & Boosting

    Machine Learning A-Z, Hands on Python & R in Data Science

    Udemy

Some MoreMy Awards and Achievements

Winner

Kick-off bot competition organised by RISC in 2016.

Winner

Egglorious Battle Innovation Challenge organised by Design Innovation Center, IIT Bhubaneswar. in the year2017..