Building systems that move data from raw feed to decision.
Forecasting, NLP on financial text, and the pipelines and dashboards that make model output usable by people who don't read Python.
Most analysis fails not because the maths is wrong, but because nobody acts on it.
I sit at the intersection of technology, business, and finance. My background is in AI and data engineering, but the work I care about is applied — models pointed at real problems.
Co-founding a finance startup and advising 100+ clients taught me the part technical training skips: turning a model's output into something a stakeholder will use.
I hold a B.Tech in AI & Data Engineering from Jain University and am now reading for an M.Tech in Artificial Intelligence (Robotics) at Bennett University. Two papers published so far — one with IEEE, one with ISJEM.
Raw feed to decision. Each stage is something I've built end to end.
Cleaning, joining, and moving large datasets so the modelling layer gets something trustworthy.
Time-series forecasting with transformer architectures, CNN-based classification, and NLP for sentiment analysis on financial text.
Equity research, investment strategy, and portfolio management, grounded in statistical modelling.
Dashboards and narratives that let non-technical stakeholders act on what the model found.
Four internships across fintech, edtech, and applied AI.
Combined business, finance, and data analysis to shape strategy across digital platforms. Built financial models and visualisations that fed decision-making, developed front-end components, and used engagement analytics to reshape social strategy.
Worked large datasets end to end — cleaning, analysis, and visualisation in Excel, SQL, and Python — then turned results into strategic recommendations.
Focused on prompt engineering for generative AI systems, closing with a hands-on project applying AI-driven text generation.
Ran exploratory analysis and visualisation across client datasets, then packaged findings into reports for non-technical audiences.
Built to test an idea, not to pad a list.
Large-scale time-series forecasting using attention-based Temporal Fusion Transformers to improve demand prediction, inventory planning, and operational decision-making across dynamic supply chains. Published in ISJEM, May 2026.
A CNN-based system classifying happiness, sadness, anger, and surprise from live video at 97%+ accuracy, built for human-computer interaction with applications in healthcare, security, and education. Published in IEEE Xplore, May 2025.
A practice-management platform for doctors that identifies returning patients by face, pulling up their records without manual lookup. Covers appointment scheduling, treatment history, and billing alongside the recognition pipeline.
One analytical framework applied across three unrelated datasets — ride demand patterns, streaming catalogue and viewing trends, and One Day International match outcomes. Built to test how far a single EDA and visualisation approach transfers between domains.
An automated trading bot executing a rules-based strategy against live market data, with configurable entry and exit logic, position sizing, and risk controls.
Grouped by domain. Everything here is something I have worked with.
A CNN-based system that classifies happiness, sadness, anger, and surprise in real time at 97%+ accuracy, with applications across healthcare, security, education, and human-computer interaction.
Applies Temporal Fusion Transformers to large-scale demand forecasting, using attention-based architectures to improve inventory planning and operational decision-making in dynamic supply chains.
Bennett University, Greater Noida.
Jain (Deemed-to-be University), Bengaluru.
Selected from a longer list. Every credential here is verifiable.
Practical tasks in quant finance modelling, predictive and statistical modelling, and data analytics strategy.
ALSO COMPLETED — Graph Theory (UC San Diego) · Trees & Graphs (CU Boulder) · Market Research & Consumer Behavior (IE Business School) · Design Thinking for Innovation (University of Virginia) · Introduction to Python Programming (University of Pennsylvania) · AI For Everyone (DeepLearning.AI) · Data Analytics Essentials (Cisco)
I'm looking for roles in quantitative analysis, ML engineering, and fintech data science. Send me a message and I'll reply within two working days.