Sai Teja Email & Phone Number

Jacksonville, Fl, Us

Woonsocket, Ri, Us

- Designed a scalable, real-time anomaly detection model using autoencoders to identify fraudulent coupons, with MLflow tracking and retraining to address drift.- Engineered a Retrieval-Augmented Generation (RAG) model with LangChain, integrating Snowflake and Faiss to enable SQL generation for non-technical users.- Led end-to-end deployment of a predictive survival model, reducing churn through weekly batch predictions and personalized email campaigns.- Built production-grade ensembled ML models for high-value customer acquisition, achieving sustained performance through quarterly retraining.- Developed a ranking algorithm based on purchase history, segmenting 200,000+ products to enable personalized recommendations.- Conducted deep-dive analyses on payment data, translating insights into actionable recommendations to improve payment success.- Designed A/B tests around ML models, defining strategies and refining campaigns with marketing teams, presenting insights to executives.- Applied causal inference to historical data, identifying trends and preferences, and evaluating vendor offers to secure future funding.- Led technical projects while mentoring junior team members, guiding them on code reviews, version control, and GenAI applications.

Manassas, Virginia, Us

• Launched a machine learning model to estimate the optimal premium pricing for insurers which centralized the underwriting process that has been in practice for a decade and made a profit of over 380,000$ in two quarters.• Designed and deployed an end-to-end machine learning model to predict the size of an insurance claim. The direct result of the model is the 28% increment in the productivity of claim adjusters.• Explored the raw unstructured XML sheets using python’s XMLTree and ensembled Lightgbm and Catboost machine learning models to boost the accuracy from 74% to 81%.• Spearheaded the common ml framework to handle the process of launching an ML model with ease and packed it as public python package that can be accessed by 8 different sister companies.• Deployed the model using flask and created a pipeline to connect the guidewire for the real-time data to make predictions and demonstrated the model’s behavior to the VP and CEO visually.• Worked solely on Bi-directional Recurrent Neural Network (LSTM) model which involved using NLP techniques like tokenizing, lemmatizing, tf-idf vectorizing, Word2vec word embedding, and BERT embedding to solve the problem of classifying risk types which resulted in reducing the time customers spent on the page by 30%.• Centralized hundreds of different data collection requests by creating SQL scripts and embedding them into a python app which dramatically expedited the process.

Fairfax, Virginia , Us

• Developed a very user-friendly web application for non-tech federal employees using R shiny for the filtering, sorting, analyzing, and retrieval of federal agricultural data.• Data collected and aggregated data from multiple federal sources of data like NASS, WASDE, AMS etc into a single structured repository.• Built regression models using linear regression and support vector machines on high-dimensional data to predict product sales.• Created a classification model using the Xgboost algorithm that classifies the products as profitable or non-profitable with an accuracy of around 84 %.• Built a time series model using prophet that accurately estimates the projections of various food product's price change in the future and integrated it into the flask web application.

Master'S Degree, Computational Science

Bachelor Of Engineering - Be, Computer Science