Ishant Jog

I am Ishant J., an AI and Data Science professional with 2 years of hands-on experience in Machine Learning, Deep Learning, and data-driven solution development. I possess a strong foundation in mathematics and statistics through my Master’s in Applied Statistics, which enables me to approach ML problems with both analytical depth and practical implementation skills.

I have worked extensively with Python-based AI ecosystems including NumPy, Pandas, Scikit-learn, and TensorFlow for building predictive and analytical models. My experience includes data preprocessing, feature engineering, model development, and performance optimization across structured and unstructured datasets. I am also familiar with data engineering concepts, ETL workflows, SQL-based analytics, and deploying scalable AI solutions.

In addition, I have been actively exploring Generative AI technologies including LLMs, RAG pipelines, embeddings, and modern AI workflows. I am highly passionate about AI innovation, quick to adapt to new technologies, and motivated to build impactful intelligent systems aligned with business objectives.

With a combination of statistical expertise, practical AI development experience, and a strong learning mindset, I believe I can contribute effectively to your team’s AI and ML initiatives.

OPTIMUM DATA ANALYTICS ( Aug 2024 To Dec 2025)

• I am engaged in business research, focusing on bridging the gap between technical and business operations.
• With a strong mathematical background, my role primarily involves developing mathematical and statistical strategies for AI and ML industry projects. did almost 3 major projects like Blood Glucose Detection (By Non - Invasive methods), 2D - 3D Construction , Patient Data Matching. Details are mentioned in CV
• Presenting Data to internal team, client in interpretable format was also a part of role.
• additionally, i ran almost 1 year series on interpretable blogs,(available on my linkedin account). where i converted very famous research papers in interpretable format which can be easily readable to non - technical people also.
• i was involved in hiring process of company where i almost took 200 - 250 preliminary interviews for positions of ML / DL Engineer , Data Scientist Intern & Data Scientist

INTERNSHIP (Feb 2024 - May 2024)

• As it was a internship, initially for 2 months i was involved in pre-processing, data analysis and visualization of kaggle datasets to check how we interns are applying theory on practical datasets.
• for next 2 months, i was involved in project titled - company acquisition. this project was having almost 2000 - 2500 different startups across almost 75-80 different countries. there are different 15 features like foundation year of company, type of company, yearly turnover of company and so on and finally there was a feature titled "Acquisition Status". this was a target feature having two values - Acquired OR Not Acquired.
• Clearly it was a classification problem. so we applied different classification algorithms like logistic regression , SVM, random forest on that and finally got max accuracy of Logistic Regression around 91%

M.Sc. in Applied Statistics & Data Science (Aug 2022 - May 2024)

B.Sc. in Physics (June 2016 - Nov 2021)