Credentials
Every claim on this site traces back to something verifiable. No inflated metrics. No vague outcomes. This page collects the formal credentials and proof artifacts behind the career narrative.
Education
Indian Institute of Technology Bombay
B.Tech in Chemical Engineering May 2021 · CGPA 7.7
IIT Bombay ranks consistently among the top five engineering institutions in Asia. Admission is through the Joint Entrance Examination - arguably the most competitive academic selection process in the world.
Admission metrics:
- JEE Advanced AIR 1382 out of ~200,000 candidates
- JEE Mains AIR 735 out of ~1,300,000 candidates - top 0.06%
What the degree actually taught: Chemical engineering is applied systems thinking. Batch processes, reaction kinetics, thermodynamics, fluid mechanics, heat and mass transfer. These are not just technical subjects. They are frameworks for reasoning about constrained systems with real failure modes. That foundation shows up directly in how I approach data systems, Machine Learning pipelines, and quant strategy research today. Every time.
Danmarks Tekniske Universitet (DTU Copenhagen)
Exchange Scholar January–June 2019
A semester exchange at DTU as part of the IIT Bombay international exchange program. DTU ranks among the top technical universities in Europe.
Lab courses completed:
- Batch Distillation
- Filtration in Filter Press
- Gas Flow in Pipes
- Liquid-Liquid Extraction
Each lab required hands-on experimental work followed by a rigorous written report. Problem setup. Experimental methodology. Raw data. Calculated results. Error analysis. Conclusions. These are the kind of reports where variance in your measurements is your problem to explain - not noise to dismiss.
DTU Experimental Reports - Available as proof artifacts:
| Report |
|---|
| Batch Distillation |
| Filtration in Filter Press |
| Gas Flow in Pipes |
| Liquid-Liquid Extraction |
These reports demonstrate early technical writing, quantitative rigor, and the habit of documenting methodology and uncertainty. Skills that show up directly in how I document machine learning systems and quantitative research today.
Professional Experience - Proof Ledger
All quantified claims on this site are drawn from the following verified record. No figures are exaggerated.
Trading results are confidential. I do not publish Sharpe, PnL, AUM, or strategy logic - so the quant rows below describe the work, not the numbers.
| Outcome | Role | Company | Period |
|---|---|---|---|
| Crypto HFT market-making & index-options MFT research | Quantitative Researcher | NK Securities | 2026–Present |
| Built & ran live systematic index-options strategies on real capital | VP Data Science & Quantitative Research | Mastertrust | Jan 2024–2026 |
| 70+ source systems integrated | Enterprise Data Automation | ANZ Bank | Jun 2021–Mar 2022 |
| 50% development & testing time reduction | Enterprise Data Automation | ANZ Bank | Jun 2021–Mar 2022 |
| 5TB+ supply chain data processed | Data Scientist | Blue Yonder | Apr 2022–Mar 2023 |
| 7 production forecasting models | Data Scientist | Blue Yonder | Apr 2022–Mar 2023 |
| 1,000+ SKUs automated | Manager Data Science | GoGlocal | Mar–Oct 2023 |
| 50% manual effort reduction | Manager Data Science | GoGlocal | Mar–Oct 2023 |
| 30% revenue efficiency improvement | Manager Data Science | GoGlocal | Mar–Oct 2023 |
| 86% AUC - graph link prediction | AI Researcher | NUS (Prof. Bryan Hooi) | Nov–Dec 2019 |
Competitions & Recognition
| Result | Event |
|---|---|
| 3rd place | NKSR Kaggle hackathon (prior to joining NK Securities) |
Certifications
These certifications were completed during the active learning phase of building data science competency. They are supporting evidence of structured learning - not the primary proof of capability. The professional outcomes above are.
| Certification | Issuer |
|---|---|
| Deep Learning Specialization | DeepLearning.AI / Coursera |
| Data Science Foundations: Data Engineering | IBM / Coursera |
| Big Data Certificate | Eckovation |
| Exploratory Data Analysis | Johns Hopkins / Coursera |
| Getting and Cleaning Data | Johns Hopkins / Coursera |
| Google Analytics Course Certificate | |
| Google Analytics Individual Qualification | |
| R Programming | Johns Hopkins / Coursera |
| The Data Scientist’s Toolbox | Johns Hopkins / Coursera |
Research
NUS - Temporal Graph Learning (Nov–Dec 2019)
Institution: National University of Singapore Supervisor: Prof. Bryan Hooi (faculty, NUS School of Computing) Topic: Link prediction on dynamic graphs using temporal attention mechanisms Dataset: College Messages dataset Result: AUC 86%
A two-month research internship during undergraduate study. The work involved implementing and evaluating graph neural network architectures for temporal link prediction. The outcome demonstrated applied Machine Learning research ability at a time when graph learning was a relatively niche area.
Technical Stack - Verified by Production Use
The following tools appear on this site only because they have been used in production work. Not as claimed knowledge. Not as coursework. Production.
Languages Python (primary), SQL, Unix shell
Machine Learning & Modeling PyTorch, TensorFlow, TFX, XGBoost, LightGBM, BERT, TSFresh, scikit-learn
Data Engineering Apache Beam, Google Dataflow, IBM DataStage, Teradata, BigQuery, Dask, PostgreSQL, Redis
Cloud & Infrastructure Google Cloud Platform (GCP), AWS, Docker, Kubernetes, FastAPI, Grafana
Quant & Finance Options pricing models, volatility surface construction, walk-forward backtesting, position sizing frameworks, microstructure analysis, Monte Carlo simulation
Publications & Public Writing
I maintain a writing practice across quant finance, machine learning, data engineering, and industry analysis. Selected pieces are available in the Writing section.
The writing is proof of a different kind. Clarity of thought. Breadth of understanding. The discipline of explaining hard ideas in accessible terms.
Academic Foundation - A Note
Chemical engineering at IIT Bombay is a four-year program with a curriculum that includes process systems, numerical methods, thermodynamics, and laboratory practice. The exchange at DTU added European lab methodology and a semester of working in a different academic culture.
Neither credential is the flashiest line on the resume by conventional recruiting standards. But they are the foundation of everything that came after. The habit of modeling systems before building them. Respecting measurement uncertainty. Checking assumptions before trusting results. These come from engineering training, not from data science bootcamps.
The DTU lab reports are concrete evidence of what that training looked like in practice. Four experiments. Four rigorous write-ups. Every calculation shown. Every source of error identified. That is still how I approach technical work.