Harshit Singh Rao

Data Scientist

Bangalore Urban, Karnataka, India3 yrs 4 mos experience

Most Likely To SwitchAI ML Practitioner

Key Highlights

Expert in Large Language Models and AI-driven solutions.
Achieved 95% accuracy in customer sentiment analysis.
Improved sales forecast accuracy by 15%.

Stackforce AI infers this person is a SaaS-focused Data Scientist specializing in AI and NLP technologies.

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Skills

Core Skills

Large Language Models (llm)Artificial Intelligence (ai)Retrieval-augmented Generation (rag)Natural Language Processing (nlp)Machine Learning

Other Skills

Python (Programming Language)Data SciencePyTorchDeep LearningNLP LibrariesSentiment AnalysisTransformersNLTKMentoringLeadershipTime Series ForecastingHyperparameter TuningNumPyMatplotlibData Cleaning

About

Hello, I’m Harshit, a data scientist specializing in Large Language Models (LLMs), Python, and Retrieval-Augmented Generation (RAG). My work focuses on designing and implementing advanced AI-driven solutions to address real-world challenges. I have a strong background in applying state-of-the-art natural language processing techniques and frameworks to create impactful, scalable systems. My passion for continuous learning has been shaped by completing specialized courses on deep learning and AI from institutions like Stanford University and deeplearning.ai. I thrive on exploring emerging technologies and leveraging them to push the boundaries of innovation in AI. Please connect with me on LinkedIn to see some of my projects and chat with me about data science.

Experience

3 yrs 4 mos

Total Experience

1 yr 8 mos

Average Tenure

2 yrs 7 mos

Current Experience

Ibm

Data Scientist

Oct 2023 – Present · 2 yrs 7 mos · Bengaluru, Karnataka, India · Hybrid

Technical Specification Automation: Spearheaded the development of an AI-powered system to generate Technical Specification Documents from SAP ABAP code dumps, cutting developer effort by 30% and accelerating project delivery timelines by 20%.
AI-Driven Code Automation: Designed and deployed LLM-based solutions to convert functional requirements into SAP ABAP code by leveraging advanced prompt engineering and diverse AI models.
RAG Pipeline Expertise: Integrated Retrieval-Augmented Generation pipelines into SAP workflows to ensure highly contextual and accurate AI-generated outputs, improving the overall quality of automated processes.
Technical Mastery and Innovation: Established a robust foundation in Python programming, specializing in LLM APIs, while delivering scalable, production-ready solutions across open and closed-source AI models to meet complex client requirements.
Cross-Functional Impact: Collaborated with SAP stakeholders to align AI tools with business goals, ensuring seamless integration into existing processes and driving measurable improvements in productivity and cost-efficiency.
Business and Strategic Value: Delivered AI-driven solutions that enhanced SAP's development lifecycle, leading to a marked improvement in developer productivity and reinforcing IBM's position as a strategic partner for AI-enabled enterprise transformation.

Python (Programming Language)Data ScienceLarge Language Models (LLM)Artificial Intelligence (AI)

Nymbleup

Machine Learning Engineer

Apr 2022 – Jan 2023 · 9 mos

Used NLP Libraries and techniques like Sentiment Analysis to analyze customer reviews. Achieved an accuracy of 95% in predicting customer sentiment.
Developed Time Series Analysis model using Python (Programming Language), PyTorch, Transformers, and Pandas to forecast sales. Improved the sales forecast accuracy by 15% and generated additional revenue with my model.
Presented data-driven insights and solutions for improving customer retention and satisfaction to senior executives, using Microsoft Power BI dashboards and reports. Received positive feedback and recognition for my work.
Performed Exploratory Data Analysis and visualization using Natural Language Processing (NLP) techniques, Pandas, and Data Visualization software, generating over 50 interactive dashboards and reports for internal and external stakeholders.
Added several Supervised Learning and Unsupervised machine learning models like XGBoost, Support Vector Machine (SVM), ARIMA, Logistic Regression, and Random Forest to the codebase in order to compare their performance using evaluation metrics such as accuracy, precision, recall, F1-score etc.,and choose the best out of them.

PyTorchDeep LearningNatural Language Processing (NLP)Machine Learning