Sriram Bharadwaj Tirumakudal Ananthapadmanabh

adlh41@r.postjobfree.com 215-***-**** www.linkedin.com/in/sriram-bharadwaj-t-959b439a Boston, MA.

Education

Northeastern University, Boston September 2019 – May 2021

Master of Science, Engineering Management (Concentration: Data Science).

Relevant Subjects: Probability and Statistics, Database Management and Database design, Machine Learning in Finance, Deep Learning and Neural Networks, Programming for Data Science, Deep Learning Specialization (Coursera), Machine Learning A-Z in Python (Udemy), Operations Research, Natural Language Processing.

National Institute of Engineering, India August 2014 – April 2018

Bachelor of Engineering, Industrial and production Engineering.

Technical Skills

Programming Languages: Python (Pandas, NLTK, spaCy, NumPy, Scikit), Java (Basics).

Frameworks: PySpark, PyTorch, TensorFlow, pyMongo.

Databases: MySQL, MongoDB.

Tools: Tableau, Power BI.

Others: Selenium, Flask, Linux, Git, Jupyter Notebook, PyCharm.

Experience

Natural Language Processing Intern November 2020 – January 2021

Stride.AI, Remote.

At Stride.AI, my project entails processing financial documents from a variety of financial institutions. To read and process the text from the paper, we run it through the Optical Character Recognition pipeline. To find all the data from the paper, the processed text is passed through the various stages of the NLP engine. My contributions to the project are as follows:

• Worked on financial data extraction from pdfs and Images using some internal tools such as Stridesdk.

• Worked on process automation solutions using Selenium, Natural Language Processing algorithms, and techniques.

• Developed FinBert-based text sentiment analysis for automated extraction of Sentiment from the text.

• When compared to the manual approach, the total task processing time was reduced by 95%.

• Developed a data pipeline in Python to extract data from MongoDB, to enable effective keyword searches on documents and financial reports.

Primary Projects

BERT-Sentiment Analysis 2021 January-2021 February

• Developed a Sentiment Analysis model using pre-trained BERT transformer to analyze the IMDB dataset.

• Built a Data loader, Optimizer and Scheduler to monitor the model's training.

• Fine-tuned model, that had been pre-trained was loaded, resulting in enhanced accuracy of 98%.

• Created a local server to deploy a small-scale model.

Database Design and Development for Retail Chain 2020 January-2020 April

• Designed, normalized, and loaded retail chain dataset on SQL Server.

• Generated statistical data with the database for, improving the probability of customers finding their favorite shoes.

• Implemented features such as stored procedures, user defined functions, data encryption and triggers for query optimization and maintaining integrity of the database.

• Generated Tableau dashboards to analyze sales, product inventory and customer data.

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