About Nabeel Khan
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Viewed 796
About me
I am a machine learning engineer from IIT, passionate about solving real world problems. I have 3 years of with industry experience in building deep learning models, Natural language processing, and time series forecasting.
Seeking opportunities to utilize machine learning in domains like finance, healthcare, transportation, pharmaceuticals, automotive, manufacturing.
Skills:
● Languages: Python, JavaScript, R, C++
● ML Libraries: TensorFlow, Keras, PyTorch, and scikit-learn.
● Deep Learning & NLP: LSTM, Transformers, CNN, YOLO, Word2Vec, Topic Modeling, spaCy, NER
● Data Management: MySQL, PostgreSQL pandas, Numpy, Excel, PySpark, Snowflake
● Tools & Data analysis: Version Control (git), Power BI, Tableau, Time Series Forecasting, Data Visualization
Education
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- Present
University at Buffalo
MS in Data Science
● GPA: 3.8/4.0 ● Coursework: Statistical Data Mining, Data Model & Query Language, Probability Theory, Predictive Analytics, Machine Learning, Reinforcement Learning. ● AWS Certified Cloud Practitioner
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- 2017
Indian Institute of Technology (IIT)
Bachelor of Technology
Achieved 99.76 Percentile in JEE (All India exam with 1.4 million candidates) for admission into IIT.
Experience
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2022 - Present
The Perfect Child LLC,
Data Analyst Intern
● Built an interactive maps application to match clients and Therapists, decreasing time and complexity of matching.
● Automated repetitive data collection from a dynamic website with Selenium, saving 30 minutes daily.
● Achieved synchronization while maintaining data segregation by combining individual google sheets files with Appscript to synchronize changes made in both the combined and individual sheets.
● Replacing a buggy software for logging therapy data, manually calculating billing hours for therapists by building a streamlined webapp hosted on AWS saving $5k/month for a small business, increasing efficiency, and gaining complete access to data. -
2019 - 2021
Merilytics
Associate Data Scientist
● Engineered an Automated Valuation Model (AVM) using TensorFlow-based custom nearest-neighbor architecture to identify comparable properties by enriching core dataset from multiple sources, and lead a team of 3
o Automated property valuation resulting in ~$25 million annual savings by decreasing ~2700 man hours weekly.
o Built the SQL ETL pipeline to extract features for real estate properties from ~100 GB of OSM US maps data.
o Collaborated with all stakeholders on biweekly calls to deploy the AVM on AWS SageMaker.
o Reported iterative improvements, and insights for adding variables on client facing weekly calls.
o Established Quality control checkpoints in the data pipeline prevent data inconsistencies and other issues.
● Developed a Demand forecast model with 1800 SKUs for a major European EV supplier.
o Leveraged Time Series forecasting packages such as fb-prophet, neural prophet, & seq2seq neural nets.
o Reduced time taken for forecasting from 1 week to 4 hours with an end to end pipeline.
● Optimized scheduling with a heuristic-based driver scheduling algorithm to incorporate business constraints.
o Produced a simulation of bills movement across 34 terminals for a long-haul trucking client.
o Removed human input for driver scheduling, optimizing time taken from multiple days to 15 minutes.
● Built Sales Forecast Model using Keras for creating business strategy for an American online clothing chain.
● Developed an end-to-end data pipeline on Azure utilizing Azure functions for daily scheduled ETL.