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Harmanpreet S.

Data Scientist | ML | Computer Vision | NLP

Moves to Sydney, Melbourne, Canberra

  • -33.7638
  • 150.924
Propose a project The project will only begin when you accept Harmanpreet's quote.
Propose a project The project will only begin when you accept Harmanpreet's quote.

Location and geographical scope

Sydney, Australia
Can work in your office at
  • Sydney and 50km around
  • Melbourne and 50km around
  • Canberra and 50km around





Skills (12)

Harmanpreet in a few words

Data Scientist with more than 2 years of hands-on experience dealing with wide array of complex datasets across various domains.

Main areas of expertise :-
- Developing end-to-end ML pipelines on cloud
- Data Wrangling & Data Visualization
- Model Development & Training
- Statistics
- Computer Vision
- Natural Language Processing

Tech Stack:-

Programming Languages: Python, SQL, HTML, JavaScript, C
Data Visualization: Matplotlib, Seaborn, Tableau
Data Analysis: Pandas, Pandas-Profiling, Tableau Prep, Dtale
Data Extraction: BeautifulSoup, Requests, Restful-Api
Web Development: React JS, Flask, FastApi, Sqlalchemy, Streamlit
Machine Learning: Scikit-Learn, NumPy, SciPy, Pandas, Yellowbrick
Deep Learning: Pytorch, FastAI, Tensorflow, Keras
Computer Vision: OpenCV, Pillow, Scikit-Image, Imgaug
Natural Language Processing: Spacy, Gensim, Transformers, NLTK
Big Data: DataBricks, Spark, MLlib
Cloud: AWS, Google Cloud Platform (GCP)
Version Control: Git




ML Engineer

Sydney, Australia

October 2020 - February 2021

-Helped a client to develop a Regression framework for ML based calibration of low cost ozone sensor using Environmental data.
-Assisted in EDA (Exploratory Data Analysis) and data visualization
-Supervised model development with final coefficient of regression resulted in getting doubled (0.62) from the initial baseline model


High Tech

Data Scientist Intern

Sydney, Australia

March 2021 - Today


1. Events Manager
- Created an event managing webapp for a news-publication client to help them in generating content based on local events.
- Implemented the app using Flask at the backend and React JS on the frontend

2. Topic Analytics
- Created a Jupyter notebook to perform auto-classfication and generate topic level insights using advanced NLP techniques.
- Generated topics from article body for 20K+ articles using LDA (Latent Dirichlet Allocation) and NMF (Non-Negative Matrix Factorization)
- Utilised NLTK & Spacy for processing text and Scikit-Learn & Gensim for implementing the unsupervised learning algorithms.

1 external recommendation

Check out Harmanpreet's recommendations