Apr 2023 – Feb 2024 · 10 mos

• As a Senior Data Scientist, I have continued to drive innovation and efficiency across our operations, focusing mainly on innovation and integrating cutting-edge technologies into LEAPS 🔗
• Responsibilities:
• 1. Performance Optimization 🚀 of virtual lab's functionalities.
• 2. Designing and implementation of recommender system development using GANs and Graph database.
• 3. Developing an end-to-end MLOps lifecycle grounded in low-code/no-code principles.
• 4. Engaging in POC development with various Large Language Models (LLMs) and generative AI tools and technologies 🤖
• Tools/Technologies: Python, Neo4j, GANs, LSTM, Text Similarity Measures, Pyspark, Python, Proprietary ML algorithms, Kubeflow, MLFlow, LLMs, Langchain,

Oct 2021 – Mar 2023 · 1 yr 5 mos

• As a Data Scientist, I have had the opportunity to significantly contribute to and lead various innovative projects, honing my skills in machine learning operations, data visualization, and computer vision.
• Key Achievements and Responsibilities:
• 1. Led the development and implementation of computer vision functionalities within LEAPS virtual lab,
• 2. Implemented end-to-end machine learning operations (MLOps) project pipelines in the automobile and manufacturing sectors.
• 3. Integration of the Plotly API to augment our platform’s data visualization capabilities.
• Tools/Technologies: Python, TensorFlow, Keras, HuggingFace, Django, Plotly, LGBM/XGB Classifiers, MLFlow, AWS

Oct 2020 – Oct 2021 · 1 yr · Indore, Madhya Pradesh, India · On-site

• As a Data Scientist at Volvo Eicher, my work was focused on developing ML algorithms to reduce vehicle downtime and proactively failure detection using vehicle telematics data.
• Responsibilities:
• 1. Developed an automated system to manage and predict part failures 🛠️, specifically focusing on turbochargers and brakes.
• 2. Implemented a predictive model 📊 to forecast inventory needs based on consumption patterns and vehicle telematics data.
• Tools/Technologies: Ensemble Methods, MLFlow, Time-Series Forecasting (ARIMA, RNN, LSTM) and NLP

Mar 2020 – Apr 2021 · 1 yr 1 mo · Delhi, India · On-site

• Research Contributions:
• 1. COVID-19 Analysis and Prediction:
• Conducted exhaustive research, employing SEIR and Regression Models for accurate outbreak predictions and assessments in India.
• Analyzed government interventions’ effectiveness, offering strategic recommendations to aid frontline health workers and policymakers.
• 2. Technological Evaluation of Contact Tracing Apps:
• Developed a structured framework to assess various contact tracing apps, evaluating their design, functionality, security, and user acceptance.
• 3. Novel Evaluation Metrics for ML Models (Ongoing):
• Working on identifying and developing innovative evaluation metrics to enhance the accuracy and reliability of classification and regression models.
• 4. Altcoins Price Direction Prediction (Ongoing):
• Leveraging machine learning techniques to predict Altcoins price movements, achieving a 71% prediction accuracy for Ethereum based on socio-economic factors, Bitcoin prices, and social sentiments.

Dec 2019 – Jul 2020 · 7 mos · Delhi, India · On-site

• Primarily, my work revolved around implementing a CNN-based model to detect and classify high-resolution satellite data for environmental planning and disaster management.
• Developed a CNN based on VGG-16 model integrated with spectral and textural information of satellite images for identification of building footprints.
• The model was trained and implemented to identify the building footprints using Worldview-3 high-resolution satellite image over part of Mumbai city of Maharashtra state, India.
• Classification accuracy from the model was nearly 94% as compared to 82% in the case of single-shot detector (SSD) algorithm.