Highly self-motivated and results-oriented graduate student with over 3 years of professional experience in Machine Learning, Data Science, Deep Learning, Natural Language Processing and Machine Learning Operations (MLOp…
Highly self-motivated and results-oriented graduate student with over 3 years of professional experience in Machine Learning, Data Science, Deep Learning, Natural Language Processing and Machine Learning Operations (MLOps). I have demonstrated success in developing and implementing algorithms across diverse domains. Skilled in Machine Learning Models, Data Preprocessing, Feature Engineering, and Data Analysis and Visualization, which drive enhanced business efficiency. Additionally, I am proficient in Software Development and Large Language Model development. I am a passionate engineer and analytical thinker, adept at applying ML/DL techniques and algorithm development to solve real-world business challenges. I bring extensive expertise in analytics, application design, and development to the table, motivated to deliver impactful solutions.
• Contribute to digital transformation platform by developing an innovative Generative AI Java Spring boot Microservices generator for a user-friendly experience. Leveraged Azure OpenAI services to streamline the process, significantly reducing development time and enhancing overall efficiency.
• Led the development of an advanced Large Language Model with LangChain, enhancing the Document Summarization process, particularly for Software Requirements Specifications (SRS). This comprehensive understanding of software requirements significantly enriched the microservice generator's capabilities in the digital transformation platform.
• Engineered a robust Scorecard Model for assessing customer creditworthiness for Calculation Dynamic Credit threshold for each customer, contributing to risk management strategies with 86% model accuracy and Conducted Weight of Evidence analysis for GAM Model on different variables to identify the most predictive features for Model development Collaborate with Business Units. This facilitated proactive risk management, fostering sustained growth for the organization.
• Developing and implementing Clustering Models based on digital customer usage patterns allowed for more precise targeting in marketing strategies. This segmentation significantly reduced unnecessary marketing expenditures by effectively directing efforts towards the right customer segments and optimizing spending 30% and maximizing returns on marketing investments.
• Created an MLOps pipeline on AWS Sagemaker to automate machine learning model training and deployment, optimizing efficiency in model management and utilization. Collaborated closely with Business Units for seamless integration and strategic alignment.
• Developed SQL procedures in Snowflake to support Dashboard development, collaborated with diverse business teams to pinpoint and prioritize critical business problems and empowering business stakeholders with data-driven decision-making tools.
• Contributed to the development and management of an ETL Pipeline using Azure Data Factory, enhancing Data Integration and Analytics Capabilities.
Contributed to a digital transformation platform by developing a Generative AI Java Spring Boot microservices generator for a user-friendly experience.
Leveraged Azure OpenAI services to streamline the process, reducing development time and enhancing overall efficiency.
Led the development of a Large Language Model with LangChain to enhance the document summarization process for Software Requirements Specifications (SRS).
Enriched the microservice generator's capabilities in the digital transformation platform through improved understanding of software requirements.
Engineered a scorecard model for assessing customer creditworthiness for calculation dynamic credit threshold for each customer.
Contributed to risk management strategies with 86% model accuracy.
Conducted Weight of Evidence analysis for GAM model on different variables to identify the most predictive features for model development.
Collaborated with Business Units.
Developed clustering models based on digital customer usage patterns.
Reduced unnecessary marketing expenditures by 30% by directing efforts toward the right customer segments.
Created an MLOps pipeline on AWS Sagemaker to automate machine learning model training and deployment.
Optimized efficiency in model management and utilization.
Collaborated closely with Business Units for seamless integration and strategic alignment.
Developed SQL procedures in Snowflake to support dashboard development.
Collaborated with diverse business teams to pinpoint and prioritize critical business problems.
Empowered business stakeholders with data-driven decision-making tools.
Contributed to the development and management of an ETL pipeline using Azure Data Factory.
Enhanced data integration and analytics capabilities.
Utilized customer data to segment customers based on their buying power.
Facilitated the identification of potential purchasers for improved marketing strategies by targeting customers.
Developed a rule-based model to predict the preferred product genre for non-sachet users.
Optimized product recommendations to ensure higher engagement, increased sales by 60%, and improved customer satisfaction by delivering relevant offerings.
Researched and engineered a deep learning-based recommendation engine with 85% accuracy on 0.5 million customers data.
Targeted new customers who had not previously purchased sachets.
Recommended relevant sachets and identified the most suitable genre for each customer.
Enhanced personalization.
Participated in the ADL AI Hackathon.
Secured a position among the top 30 teams.
Showcased innovation and problem-solving skills.
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