chetna priyadarshini

Data Scientist

https://www.opentalent.in/chetna-priyadarshini

Cerence

Key Strengths

Strong foundational knowledge in Machine Learning and Deep Learning, demonstrated by building a neural network from scratch.
Extensive experience with Large Language Models (LLMs), including fine-tuning (LoRA, ReFT, BERT), RAG systems, and advanced query engines (LlamaIndex).
Practical experience with MLOps tools like Airflow, MLFlow, Docker-Compose, and FastAPI for deploying AI pipelines.
Proficiency in Python and Jupyter Notebooks for data science and machine learning tasks.
Directly relevant experience as a Data Scientist at Cerence, aligning with the target role.

Cultural & Operational Fit

Cultural Fit Analysis

The candidate's projects demonstrate a strong focus on cutting-edge AI/ML technologies, particularly in NLP and LLMs, which aligns well with an innovative, research-driven culture. The diversity of projects, from foundational neural networks to MLOps and advanced RAG systems, indicates a broad interest and willingness to explore different facets of data science. However, the projects are predominantly personal, and there is limited information on collaborative work or contributions to open-source communities beyond personal projects, which could be a factor in cultural fit for highly collaborative environments.

Soft Skills & Operational Fit

The candidate's project descriptions indicate a proactive and self-driven approach to learning and applying advanced ML concepts. The MLOps project suggests an understanding of deployment and operational aspects of ML solutions. However, without specific assessment data on communication, logical reasoning, or teamwork, a comprehensive evaluation of soft skills and operational fit is limited.

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Projects

chetnapriyadarshini

March 21, 2026 – Present

Introducing myself and my work

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Thesis_PeFT

March 21, 2026 – Present

Thesis project for MS on LoRA and ReFT fine tuning

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LoRA_fine_tuning_on_Bloom

March 20, 2026 – Present

LoRA fine-tuning on BLOOM 1B using HuggingFace PEFT — demonstrates low-rank adapter injection, trainable parameter reduction (~0.1%), and efficient adaptation of a 1B-parameter model.

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Fine-Tuning-Large-Language-Models

March 19, 2026 – Present

End-to-end fine-tuning of a pre-trained LLM on a customer experience dataset — instruction formatting, training with HuggingFace Trainer, and before/after inference comparison.

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Llama_Index_Query_Engine

March 18, 2026 – Present

Advanced QA agent using LlamaIndex's SubQuestionQueryEngine — decomposes complex multi-part queries into sub-questions, retrieves targeted context per sub-question, and synthesises a unified grounded answer.

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Insurance_RAG_agent

March 17, 2026 – Present

A RAG-based QA system for insurance policy documents using OpenAI embeddings, ChromaDB vector search, CrossEncoder re-ranking, and GPT response generation.

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BERT_finetuning

March 10, 2026 – Present

Fine-tuning a pre-trained BERT model for sentence pair classification tasks including NLI and semantic textual similarity, using the Hugging Face Transformers library and PyTorch.

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MLOPS_OpenSource_Pipeline

July 10, 2025 – August 24, 2025

A project demonstrating how to deploy an AI pipeline using open source tools: Airflow, MLFlow, Docker-Compose, FastApi

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Bike-Share-Neural-Network

February 8, 2017 – Present

Feedforward neural network built from scratch in NumPy — manual forward pass, backpropagation, and gradient descent — to predict bike-sharing demand. No frameworks used (Udacity Deep Learning Nanodegree).

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WeatherBuddy

April 13, 2016 – Present

Android weather app with 14-day forecasts, Google Maps, GPS location, OpenWeatherMap API, and two home screen widgets (today + weekly).

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Key Strengths

Strong foundational knowledge in Machine Learning and Deep Learning, demonstrated by building a neural network from scratch.
Extensive experience with Large Language Models (LLMs), including fine-tuning (LoRA, ReFT, BERT), RAG systems, and advanced query engines (LlamaIndex).
Practical experience with MLOps tools like Airflow, MLFlow, Docker-Compose, and FastAPI for deploying AI pipelines.
Proficiency in Python and Jupyter Notebooks for data science and machine learning tasks.
Directly relevant experience as a Data Scientist at Cerence, aligning with the target role.

Cultural & Operational Fit

Cultural Fit Analysis

Soft Skills & Operational Fit

chetna priyadarshini

Key Strengths

Cultural & Operational Fit

Top Skills

Skills

Experience

Projects

Key Strengths

Cultural & Operational Fit