Data Scientist / Machine Learning Engineer


Seeking a challenging position in the field of machine learning and data science, where I can leverage my passion
for learning and apply my interdisciplinary skills to solve complex problems and drive meaningful insights from data.

Data scientist with hands on experience in machine learning and statistical modeling in Python (2+ yrs)
Physicist with strong varied scientific background, having successfully worked in multiple disciplines (5+ yrs)
Working knowledge of digital signal processing and image processing in Python (1+ yrs)
Experience in cloud-computing with Amazon Web Services (S3), Docker, MySQL, Snowflake SQL, and Prefect (1 yr)
Prototyping and patenting (4+ yrs)

Ph.D., Physics, Northeastern University, Boston, MA, US

Senior Data Scientist
Imaging the Battery Interior for Defect-Detection through Digital Signal- and Image-Processing:
1. Led data analysis of Lithium-ion battery defect detection, successfully identifying internal anomalies including gas pockets, incomplete
wetting, tears, and impurities.
2. Scaled computation by 10x through automating the time-series analysis, digital signal processing and feature extraction, and
developing image visualization pipeline for ultrasound signals.
3. Created toolboxes for image adjustments and rotation, as well as new image processing tools for blob detection and statistical analyses.

Orchestration and Data Ingestion to AWS/Snowflake:
1. Facilitated companys migration to cloud-based computation by writing production codes for data ingestion and transfer. This resulted
in a 10x speed increase and easy data accessibility via SQL and AWS libraries.
2. Developed production codes and Prefect flows for data ingestion, loading large datasets (json, csv, txt, xlsx, etc.) from S3 buckets,
extracting metadata, and transferring data based on file name or content.
3. Pioneered the company's first ETL orchestration pipeline and created a slack status alert notification platform.

Ad-hoc Projects:
1. Enhanced the knowledge of battery quality by developing codes to calculate signal attenuation, and animated thermal fluctuations.
This was a success in proving the company with an enhanced knowledge of battery life cycle.
2. Improved ML models for state of health and charge estimation via hyperparameter tuning of XGBoost model. Collaborated in
developing a deep learning model for feature extraction using autoencoders and SoH estimation.
3. Supported the engineering teams by providing data-driven insights and python toolboxes via scientific programming and.
visualization, including in transducer specification characterization.
4. Created terahertz signal analysis and cell layer imaging pipeline in collaboration with Fraunhofer institute in Germany.
5. Assisted in maintaining, upgrading, and refactoring in-house codebase libraries through version control (GitHub, CI/CD).

Graduate Research Assistant, Northeastern University, Boston, MA
Successfully led award-winning multi-disciplinary research for the development of new generation smart optical sensors in the
Laboratory for 2D Quantum materials that resulted in a GapFund360 prototyping grant, two U.S. patents, and multiple publications in
prestigious scientific journals.
Pioneered the color and spectrum detection research by developing various ML codes (classification via support vector machines,
Bayesian statistics/naïve Bayes, neural networks, k-nearest neighbors, etc.) for pattern recognition from quantum materials/sensor data.
Developed multiple toolboxes to operate, extract and analyze nanomaterial sensor data. Designed, and built high-accuracy data
acquisition system, and performed the experiments.
Simulated and statistically analyzed semiconducting materials' behavior to design novel color and spectrum estimation sensors, hence,
improved the accuracy and reduced the engineering complexity of the existing tools.
Conducted customer discovery and managed laboratory operations as a safety officer and liaison.

2022, Elsevier Materials Today
2019, IOP Machine Learning: Science and Technology
2019, ACS Applied Nano Materials


Looking for the next opportunities as machine learning engineer or data scientist, only full-time positions. I have 4-5 years of experience working with data -- almost 2 years in the in the industry and the rest in academia. I have a PhD in Physics, with concentration in solid states/semiconductors, and I prefer to be in an industry segment in which my Physics knowledge has value, though it is not a hard requirement.

Employment Preferences
Academic Degree

Total Professional Experience

8 years

Startup Experience

2 years

Big-Tech Companies

no experience

Enterprise Experience

no experience
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