NAME Email : null null Linkedin : null EDUCATION • University Place Master of Science in Computer Science August 2018 – December 2019 • University Place Bachelor of Engineering in Information Technology August 2014 – July. 2018 EXPERIENCE • Company Place Data Science Specialist February 2022- Present ◦ Resume Classifier Model Deployment : Deployed a recommendation model that recommended suitable candidates for each position in the form of an Excel sheet using Google Cloud’s Vertex AI and Kubeflow pipelines. ◦ Truck Forecasting Slots Prediction : Improved an LSTM model’s time-series forecasting of available truck slots by performing feature engineering using BigQuery • Company Place Data Scientist April 2020 - February 2022 ◦ Content Summarizer : Used pretrained BERT Huggingface encoders and PyTorch to implement an abstractive text summarization solution that generates two-line summaries of text with 92 percent accuracy ◦ Recommendation Engine : Developed a content recommendation engine using Python’s SpaCY library and fastText embeddings to dynamically and accurately display similar frameworks ◦ Automated Risk Assessment : Developed a risk assessment model framework in Python with Scikit-Learn for a client in the cash logistics sector. • Company Place Software Engineering Intern June 2019 - August 2019 ◦ Model Validation : Added a new validation component to the code base to evaluate 80 network threat classification models against a golden dataset • Company Place Data Science Intern June 2017 - August 2017 ◦ Customer Feedback Analytics Tool : Created an interactive tool in R Shiny that performs text analytics on client feedback using R’s tidyR libraries for wrangling text data to generate real-time results ◦ Employee Turnover Predictor : Created a model that uses ARIMA to generate a model from time-series data to predict employee turnover on a monthly basis. The model performed with an overall accuracy of 88 percent PROJECTS • Master’s Research Project - Uncovering Bias in Twitter bios (R, Pyspark) : 600,000 Twitter bios along with the users’ gender, age and political affiliation were imported using Pyspark. By performing two kinds of short text topic modelling, Scholar and Biterm Text Modelling (BTM) in R, 15 distinct groups and words strongly associated with these groups were identified The output was a list of words with their classification based on their associations along gender and political lines • Real Time Twitter Analytics (R, R Shiny) : Developed an application that captures tweets about a user-specified topic in a time-frame set by the user and then performs analytics on them in real-time and presents the results through an interactive tool. Analytics include statistics about the captured tweets, three kinds of sentiment analysis and a network of interrelated words CERTIFICATIONS Google Cloud Certified Professional Machine Learning Engineer Azure Data Scientist Associate PROGRAMMING SKILLS • Languages : Python, C++, SQL, R, Scala Technologies : Azure, AWS, Tensorflow, Keras, NLTK, Spark, Docker, Hadoop, Spark, Kafka, Airflow, Hive, Impala, Jenkins, Git, PowerBI, Tableau