Nilpa Jha

Product Manager

Seattle, Washington, United States9 yrs 7 mos experience

Most Likely To SwitchHighly Stable

Key Highlights

Finalist at International Data Analytics Olympiad 2019
Published research at KDD and ACCV
Winner of multiple featured ML Hackathons

Stackforce AI infers this person is a Senior Machine Learning Engineer with expertise in E-commerce and AI-driven solutions.

Contact

nilpajha@gmail.com LinkedIn

Skills

Core Skills

Machine LearningNlpDeep LearningComputer Vision

Other Skills

AWSActive learningAirflowAlgorithmsAndroidAzure machine learningC++D3.jsData StructuresData VisualizationData ingestionElastic searchFeature engineeringGANIllustrator

About

Nilpa is experienced Senior ML Engineer with a demonstrated history of working in the Internet, E-commerce, Retail , Pharma and Automation Industry. With emphasis on experimentation and implementation of novel methods, she has publication at KDD and ACCV. She was the Finalist at International Data Analytics olympiad 2019, and winner of other featured ML Hackathons. Skills : • Model Lifecycle : Web scraping, Data ingestion, Data Visualization, Feature engineering, Model Training and testing, Metric evaluation, Inferential statistics, Model versioning, Model deployment, Active learning, Machine Learning System, Distributed system • Tech stack : SQL, Python, Airflow, MLFlow, RESTful api, Elastic search, AWS, Java • Deep learning : GAN, Transformer, YOLO, Seq2Seq, siamese network, multi-modal learning, GraphNN • Computer vision : Image classification, Image generation, object detection. • NLP : Bag of words approaches, text classification, text similarity. • Classical ML Approaches: Gradient boosting, Random Forest, clustering, Linear Regression website: https://nsquare.github.io/

Experience

9 yrs 7 mos

Total Experience

1 yr 10 mos

Average Tenure

4 yrs 7 mos

Current Experience

Google deepmind

Research Engineer

May 2024 – Present · 2 yrs 1 mo

Google

Machine Learning Engineer

Nov 2021 – Present · 4 yrs 7 mos · Seattle, Washington, United States

Experienced Applied Scientist at Google research, working on multimodal content understanding in collaboration with search and YouTube shorts

Georgia institute of technology

Graduate Research And Teaching Assistant

Sep 2021 – Aug 2022 · 11 mos

Working for CS7280 under Prof Dovrolis

Glassdoor

Machine Learning Engineer

Nov 2020 – Sep 2021 · 10 mos · Seattle, Washington, United States

Working as MLE in content machine learning team. Working on applied NLP products :
Review relevancy and informativeness for company FAQ page:
https://www.glassdoor.com/FAQ/Glassdoor-Questions-E100431.htm
Review Highlights:
Extracting Pros and Cons from Reviews for featured snippets.
Model Lifecycle : Web scraping, Data ingestion, Data Visualization, Feature engineering, Model Training and testing, Metric evaluation, Model deployment, Active learning
Tech stack : SQL, Python, Airflow, MLFlow, REST api, Elastic search, AWS, Java

Web scrapingData ingestionData VisualizationFeature engineeringModel Training and testingMetric evaluation+12

Ikea group

Senior Data Scientist- Visual Intelligence

Aug 2019 – Apr 2020 · 8 mos · Sweden

Upgrad.com

Student Project Mentor/Guide

Nov 2017 – Jun 2019 · 1 yr 7 mos

Guide students on their Machine learning capstone projects.

Myntra

Deep learning engineer

Sep 2017 – Aug 2019 · 1 yr 11 mos

Part of team RAPID@ Myntra responsible for generating and curating AI based designs using GANs and ML.
Team : Part of Computer Vision team :
Working in Computer vision team of Myntra for design automation using Deep learning frameworks of GAN.
Worked on DCGAN, ACGAN, SRResnet-GAN ( Variants for Generative Adversial Framework)
Experience of working in VGG and Resent frameworks.
Experienced in working with Tensorflow and Pytorch.

Accenture technology labs

Senior Cognitive Engineer

Feb 2016 – Sep 2017 · 1 yr 7 mos · Bangalore

1) Deep Learning:
Prototyping Text classification module using convolution neural networks on bug reports data vs severity. -
Developing semantic similarity module using word embedding.
2) Text Mining :
LDA for topic modelling on 3-gram text from feedback/review data.
hash tag model for social media posts based on POS tagged noun words and built Rshiny application for the various plots.
3) Data Visualization :
Created basic EDA tool for ticket inflow data using GGPLOT2 and Rshiny
Interactive dash boarding using googleVis ( Google charts ) for visualizing text data from LDA model
4) Machine learning:
k-means, hierarchical clustering on historical data based on the project technologies and verticals.
Worked on predictive modelling of project health based historical data of project data. (Benchmarked for accuracy against Random forest and logistic regression) and implemented its web services via Microsoft azure machine learning studio.
Bug reports classification based on Random Forest.
Automation of Test -cases and Agile stories mapping using Cosine similarity score.