Before pursuing the MSE in Data Science program at Penn, I worked as an Associate Data Scientist at iQGateway - an IT solutions company. I helped build a next-generation Automated Machine Learning platform that involved pipeline automation and patented model evaluation based on multiple metrics. I worked on creating a modular feature transformation library for transforming data in Python and conducted extensive research on Machine Learning. This included optimizing MLOps workflow to enable an easy transition from model development to inference for production environments. I not only learned how to use existing AutoML tools like Dataiku, but also used various libraries in the Python Data Science ecosystem (NumPy, pandas, scikit-learn, PyTorch - to name a few) for developing software at a professional level. Working in a collaborative environment helped me develop critical programming skills; using version control systems like GitLab highlighted the importance of good coding practices, building documentation using the sphinx library taught me the value of well-documented work, and conducting unit-tests using the pytest library maintained an agile process while establishing code standards across the development team. Additionally, I used tools that support the Data Science pipeline like Weights and Biases - an ML experiment tracking platform, to create benchmarks of different Machine Learning experiments, which aided in streamlining model debugging and tuning.