Data Engineer

PRIYANKA PHADNIS

DATA SCIENTIST

D

DD

PRIYANKA PHADNIS

DATA ENGINEER

SUMMARY

Data Engineer with a combination of

software and data skills. Professional

experience of 4+ years. Master’s degree in

Data Science. Skilled with building and

deploying Restful webservices, Python

scripts, SQL, Airflow DAGS. Proficient in

Machine learning, data analysis. Open to

relocation anywhere in the United states.

CONTACT

***************@*****.***

Los Angeles,CA

linkedin.com/in/priyankaphadnis/

https://github.com/pinks1995

EDUCATION

2021

WPI [Worcester, MA, USA]

Master of Science in Data Science

2017

VJTI [Mumbai,MH, India]

Bachelor of Technology in Electronics

MANAGEMENT SKILLS

JIRA, Git, Gitlab, MS Office, Excel, Rally,

MS Powerpoint.

TECHNICAL SKILLS

Programming languages: Java, Python,

SQL, Typescript, MySQL, CSS

Frameworks: Spring Boot, MVC,

Hibernate, ReactJS, Hadoop, Spark,

Apache Kafka, PyTorch, Tensorflow

Data Visualization: Seaborn,

Matplotlib

Data Manipulation: Pandas, Numpy,

Scikit-learn, Spacy, OpenCV

Modelling: Scikit-learn, Keras

Cloud: Google Cloud Platform, Cloud

composer, Airflow, Google Dataproc

Databases: Cloud SQL,PostgreSQL

Data Warehouse: Google BigQuery

WORK EXPERIENCE

Data Engineer Aetna/CVS Health,Remote, September 2022-Present

Created utility packages using Python which read Google Dataproc cluster properties from text file and implemented it in all Apache Airflow DAGS which are used to target users in CVS’s experimentation framework.

Implemented checkpointing using PySpark, using Google Biquery and Google Cloud Storage buckets to prevent mismatch in data between two subsequent write operations, which caused incorrect information to be used. Software Developer Optum, Cypress, Orange County, July 2021 – August 2022

Part of operations team, where I successfully developed end-to-end webservice, which enables doctors to view all medical reports in one PDF, by merging all reports into one report for each patient in Spring boot and Kubernetes.

Developed and deployed webservices which enabled machine assisted prior authorization of medical tests by doctors and nurses. Data Science Intern Homesite Insurance, Boston, Feb 2021 – May 2021

Redesigned parts of Homesite’s homegrown insurance peril risk rating package in R to integrate H2O and reduced model building time by 50%

Implemented H2O’s ElasticNet regularization to improve original general linear model performance for Fire Frequency and Severity prediction models. Data Science Intern Eatiquette, Worcester, Aug 2020 – Dec 2020

Developed a python script to extract ingredients from 1000 food product descriptions. Cleaned the extracted text using custom regex rules and pandas and reduced it to a list of 8000 unique ingredients from 20,000 ingredients.

Software developer Citiustech, Mumbai, July 2017-March 2019

Successfully developed and deployed multiple webservices in Spring Boot which retrieved patient data from Mirth channel and helped database team monitor and analyze the quality of HL7 data attributes of patient’s medical reports and Electronic Health Records.

DATA SCIENCE PROJECTS

Salary prediction using Regression in Python

Developed a salary prediction model in Python that helps the talent acquisition, HR department in improving their compensation strategy to attract the best talent and reduce attrition rate with scikit-learn.

Built a Random Forest Regressor with mean squared error of 367 over a baseline of 385 from linear regression when trained on a dataset with a million records. Predicting Telecom Customer Churn using Python- Feb 2019

Developed models to predict telecom customer churn in the next month using Logistic Regression, XGBOOST and Sequential Neural Network using Keras and Scikit-learn with accuracy 76%,78% and 79% respectively. Airline Sentiment Analysis using Text Sentiment Analysis

Performed Sentiment Analysis on text car reviews using text cleaning,Vader Sentiment analysis, and generated tags for car reviews which help you summarize a review quickly.

Implemented BM25 for most relevant review search using a keyword.

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