Meenakshi P

I am a research-driven Data Scientist with a strong academic background, currently transition- ing into the field of industrial data analysis. Over the past nine years, I have developed strong expertise in research-driven data analytics, combining advanced astrophysics tools with three years of practical machine learning experience. I possess deep expertise in managing large- scale datasets, pattern recognition, modelling, model testing and performance evaluation and extracting actionable insights from complex data.

Throughout my research, I have analysed large-scale stellar datasets obtained from world-class telescopes to study chemical compo- sitions, orbital parameters, and kinematics of stars - critical for understanding their origins and tracing the formation and evolution of our galaxy. This work has involved rigorous data anal- ysis, modelling, and interpretation of stellar surface chemical compositions, requiring a deep understanding of physics, mathematics, and statistical techniques, alongside strong program- ming and software expertise. I possess the technical proficiency to handle and analyse massive astronomical datasets from large-scale sky surveys for deriving robust scientific insights.

I am now looking forward for a data scientist role to apply statistical knowledge, machine learning, and programming skills to solve real-world problems. I am open to acquire and master ad- vanced tools to deepen data analysis skills and deliver impactful outcomes that advance my professional development.

1. I am currently working as a National PostDoctoral Fellow at Christ (Deemed to be University), Bangalore from April 2024 onwards.

   My key responsibilities include: 

   • Preprocess extensive datasets from large sky survey to ensure high integrity inputs for a detailed data analysis.
   • Conduct exploratory data analysis to uncover patterns, detect anomalies, and inform feature engineering for subsequent modelling.
   • Identification and classification of metal-poor stars using supervised machine learning techniques.
   • Predict stellar parameters from spectral data employing regression models.
   • Developing computation tools to map stellar kinematics using Python scripts.
   • Training and validating models to infer stellar origins from the surface chemical composition data of the stars.
   • Visualising the results using Matplotlib, and extracting insights through compelling data representations.
   • Authored and Published research findings in peer-reviewed international journals and present results in National and international conferences.
2. I have been working as a PostDoctoral Fellow from 2023 - 2024 at Christ (Deemed to be University), Bangalore. My key responsibilities included:
   • Acquired and cleaned large-scale sky survey data, preparing it for robust and precise analytical workflows.
   • conducted exploratory data analysis to uncover patterns, detect anomalies, and inform feature engineering for subsequent modelling.
   • model stellar parameters via regression algorithms, enhancing classification accuracy for metal-poor stars.
   • Developed Python scripts to estimate orbital parameters and kinematic properties of stars.
3. I have been working as a PostDoctoral researcher at Indian Institute of Astrophysics, Bangalore from 2022-2023. My key responsibilities included:
   • Reduced data into useful spectra using IRAF software.
   • Developed Python scripts to estimate photometric temperature of the stars.
   • Determined stellar atmospheric parameters using MOOG.
   • Identified stellar type and classified the stars into different stellar groups.
   • Authored and Published research findings in peer-reviewed international journals and present results in National and international conferences.

I completed my PhD in stellar Astrophysics at Indian Institute of Astrophysics and got doctoral degree on 03/09/2022.