Nischay Girish Gowda Experience - * + years

Email: ade1hu@r.postjobfree.com Place - Bangalore, India Mobile: 889-***-****, 866*******

LinkedIn: https://www.linkedin.com/in/nischaygirishgowda GitHub: https://github.com/nischaygowda105

CAREER OBJECTIVE

A passionate data scientist having knowledge in predictive modelling, data processing, and data mining algorithms to solve challenging business problems. Strong background in Python and knowledge of various types of machine learning and Deep Learning Algorithms. SUMMARY

1. Good understanding of Statistics, Machine learning and Deep learning concepts. 2. Hands on experience in python frameworks like TensorFlow, Scikit-Learn, Pandas 3. Basic understanding on ML libraries like Pytorch, spark. 4. Data analysis and Visualization.

5. Cloud Deployment of ML and Dl models

TECHNICAL SKILLS

Skill Technology known

Programming /Scripting Python

Tools /IDE PyCharm, VS Code, Jupyter Notebook

Data Science Machine learning, Deep Learning, Statistical modelling, Computer Vision,,Pandas, Scikit-Learn, Keras, Bokeh, TensorFlow, Time series, Pytorch

Operating system Windows, Ubuntu

Cloud AWS, GCP

Version Control Git, Bitbucket

Data Base PostgreSQL, MySQL

PROFESSIONAL EXPERIENCE

Radome Technologies and Services Pvt Ltd (June 2019 - Present) Role: Machine Learning Engineer / Data Scientist

• Understanding client requirements, involved in business strategies with product managers and building end to end solution as per the business problem.

• Involve in brain storming sessions (Think Tank) for product development and features discussion.

Projects

1. Custom Object detection in Real-time:

Solved multiple problems as per the client requirement. Built Object detection model for detecting a single Fighter jet (Su-30) using TensorFlow API with 90-97% accuracy in real time video. Then trained the model to detect two different fighter jets (Su-30 and Mirage 2000) with 80-90% accuracy Technology used: Python, Keras, OpenCV, TensorFlow, NumPy, Mask R-CNN. Domain – Aerospace and Defense

2. Forecasting using Historic Data:

Solved multiple problems as per the client requirement. Predicting inventory stock in advance by using pattern recognition from historic inventory data (20 years). Univariate prediction (one independent variable) and Multi-variate (multiple independent variable) using Machine learning techniques and multi-threading for faster computation. Technology used: Python, Sklearn, Keras, NumPy, Bokeh, RNN, LSTM, Random Forest, XGBoost, Time Series Forecasting.

Domain: AI in Supply Chain Management

CERTIFICATION

Digital Vidhya (Dec 2018 – May 2019)

Data Science (Certification)

● Involved in building data analysis using Python and Machine Learning.

● Sentiment analysis of Amazon reviews data using Python and NLTK PERSONAL PROJECTS

• Digit Recognizer: Train an Image Recognizer model using CNN. MNIST dataset having 60,000 images with 10 classes used. Achieved accuracy of 91%. The accuracy was further improve using data augmentation on train data to 96%

Techniques used: Python, CNN algorithm, Keras, TensorFlow Accuracy on test data: 96 %.

• Toxic-Comment-Classification: Dataset of comments from Wikipedia’s talk page edits. Build a multi-headed model that’s capable of detecting different types of toxicity like threats, obscenity, insults, and identity-based hate. Final output for model is to predict a probability score of each type of toxicity for each comment.

Techniques used: Python, Regex, Word Tokenizer, TF-IDF weighted AvgWord2Vec, Naïve Bayes, Logistic Regression.

EDUCATION

• Bachelor of Engineering (2014 – 2018)

Vemana Institute of Technology (VIT)

Electronics and Communication - 74.87 % (First Class with Distinction)

• Pre- University (2012 – 2014)

Krupanidhi Pre-University College

Electronics and Communication (ECE) – 90.07 %

• High School

Janak Vidyalayaa

10th(science) – 90.05%