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About
I build machine learning that makes it out of the notebook.
I'm Ojasv, a data scientist and AI/ML engineer. Right now I build the AI models behind a live precision-agriculture platform, turning raw field measurements into decisions that growers act on every day.
Over four years I've shipped more than 30 projects end to end: fine-tuned language models that beat research baselines, fraud and churn models running on real users, retrieval-augmented assistants, and the data pipelines underneath them all. I finished my Master of Data Science with a perfect 4.0.
I'm inspired by nature, and it shows in how I work. Rivers carve canyons one pass at a time; trees grow a ring a year. Good models are built the same way, with patience and steady iteration. That's why this site is a trail guide: every section is a stop on the route, and the map at the bottom lets you jump anywhere.
- 01If you can't explain a model, it isn't finished.
- 02Shipped beats perfect. Then make it better.
- 03Zero unfinished projects. Still true.
Trailhead · pick a route
Services
what i build, whether you're hiring full-time, on contract, or for a single project.
How a trip works
- camp 01Briefa free 30-minute call about the problem and the data
- camp 02Proposalscope, timeline and a fixed quote, in writing
- camp 03Buildweekly updates and a shared repo from day one
- camp 04Handovercode, docs and a walkthrough, so it stays yours
Selected Work
Field diary · five projects, with results
Reddit UBC Reporter
Serverless GenAI · UBC CIC hackathon · Nov 2025
A week of r/UBC → one categorised email digest, no servers to run
A serverless AWS pipeline that fetches a week of r/UBC posts, has Llama sort them into categories, then hands each group to Claude to write a summary of the main themes, insights and tone, emailed out on a schedule.
Built with a team of five at the UBC Cloud Innovation Centre's Fall 2025 Generative AI Hackathon: four independent Lambda functions on AWS SAM, EventBridge schedules, S3 storage and Postmark for email.
Crop Yield Forecasting
Vision transformer · Climate ML · 2026
An ICCV 2023 paper, rebuilt end to end in PyTorch
A reimplementation of MMST-ViT, a multi-modal spatial-temporal vision transformer that forecasts crop yield from Sentinel-2 satellite imagery, HRRR daily weather, long-term climate records and USDA crop statistics.
A Pyramid Vision Transformer backbone is pre-trained with SimCLR contrastive learning, then spatial and temporal transformers aggregate each county across the growing season. Evaluated on RMSE, R² and Pearson correlation.
Asteroid Hazard Classifier
Classification · Imbalanced data · Symbiosis
89% recall at 99.7% precision, when only ~0.2% are hazardous
A classifier for potentially hazardous asteroids, trained on 958,524 NASA/JPL records where roughly one in five hundred is hazardous. Great for the planet, terrible for a naive model.
RFECV picked 30 features, a scaler shoot-out crowned the robust scaler, and XGBoost won the benchmark. Minimum orbit intersection distance and absolute magnitude came out as the strongest risk signals. B.Tech research with a team of four.
DisasterDash
Dashboard · Data viz · UBC MDS · 2026
Where disaster aid falls short of the losses, on one map
An interactive dashboard over EM-DAT that shows where aid responses fall short of actual economic losses, so policy workers can filter by disaster type, dates and countries and compare the gap directly.
It includes an AI Explorer that answers questions about the data in plain English through the Anthropic API. Built in Shiny for Python with a team of three for DSCI 532, with Playwright and pytest tests.
Global Temperature Forecast
Forecasting · Time series · Dec 2025
10.56 °C land average forecast for 2030, about 2 °C above baseline
222 years of Berkeley Earth daily land temperature, from 1800 to 2022, and one question: what does 2030 look like? Daily anomalies were rolled into yearly averages to keep the long-term trend and drop the seasonal noise.
Linear regression, random forest and SVR were compared on RMSE, MAE and R²; SVR won. Built with a team of four as a reproducible pipeline: Docker, a Makefile, pytest and a Quarto report.
entry 1 of 5
Summit register · signed by clients
Client reviews
every peak keeps a register. these are notes left by the researchers and teams i've built things for.
Ojasv implemented BERT-based sequential sentence classification and LDA topic modelling pipelines for my NLP research at Radboud. The tokenisation logic, attention-mask handling, and hyperparameter sweep were all production-quality. Invaluable for my PhD thesis.
Ojasv handled cross-lingual scraping, bilingual Arabic-English preprocessing with custom tokenisation, and fine-tuned XLM-RoBERTa for multilingual sentiment classification. The F1 scores on Arabic test data exceeded our in-house baseline by a significant margin.
Ojasv built custom Power BI DAX measures, parameterised SQL queries, and Python automation scripts tailored to State Street's analytical workflows — then walked me through each piece clearly.
Ojasv independently delivered a deep learning pipeline for Stock Price Prediction — LSTM architecture with sliding-window sequence encoding, dropout regularisation, and backtesting. Exceptional work.
Work Experience
Survey map · 2022 — present
tap a card to open its field notes ↓
- FreelanceData scientist · self-employed · 2022 – 2026PhD theses, fintech dashboards, zero unfinished projects
Designed and deployed production-grade machine learning systems, specialising in deep learning, computer vision (CNNs, YOLO), NLP and LLM-powered RAG pipelines that improved model accuracy and cut manual workflows. Built scalable ML pipelines and ETL architectures across Snowflake, PostgreSQL and Neo4j, with real-time analytics in Tableau and Power BI, for enterprise and startup clients.
- BERT sequential sentence classification and LDA topic modelling for a PhD at Radboud University
- Fine-tuned XLM-RoBERTa for Arabic–English sentiment, beating the in-house F1 baseline for a Maynooth University researcher
- Power BI DAX measures, parameterised SQL and Python automation for State Street workflows
- LSTM stock-price forecasting with backtesting, and a full real-time traffic signal optimisation thesis
- Edtech work with Escalera Technologies and Tech Learniversity
- Spartan PokerData analyst · internship · Jan – Jun 2024a payout model 50K+ players actually use
A six-month on-site internship in Gurugram, working on a live real-money gaming product with real player data.
- Engineered a first-of-its-kind game-theoretic payout model, adopted by 50K+ users
- Built churn prediction models that cut player attrition by 10–30%
- Deployed fraud detection pipelines, boosting detection accuracy by about 60% over rule-based systems
- Led the Snowflake migration and QuickSight BI deployment, cutting query latency by 60%
- Univ. of British ColumbiaGraduate TA · CBTF proctor · Sep 2025 – May 2026helped scale the testing centre 70% in one term
Two roles alongside the Master of Data Science: graduate teaching assistant at the Computer-Based Testing Facility, and social representative for the MDS X cohort.
- Supported the CBTF's rapid expansion in a single term: 22 → 31 courses (+41%), 155 → 263 exams (+70%), 4,298 → 6,094 students (+42%)
- Kept secure, high-volume assessment delivery running across 38,000+ student exams
- Social representative, MDS X (Sep – Dec 2025): led and organised social and academic events with fellow reps for the cohort
- PavePalData scientist · capstone · Apr – Jun 2026grounded AI for road maintenance decisions
A full-time capstone with PavePal as part of the UBC Master of Data Science: Grounded AI for Road Maintenance Decisions: Evaluating Retrieval Quality and Failure Modes.
- Evaluating how well a retrieval-augmented system grounds its answers for road-maintenance decisions
- Measuring retrieval quality and cataloguing the failure modes that matter before anyone relies on it
- VintalityData scientist · contract · Apr 2026 – nowbuilding the AI behind a vineyard dashboard
Data scientist at Vintality: Tech + Terroir, working remotely from British Columbia on precision viticulture.
- Building the AI models behind Vintality's dashboard, the layer that turns raw vineyard measurement into grower-facing intelligence
- Developing models end to end, from data ingestion and feature engineering through training, evaluation and the served endpoint
- Working across an agtech sensing stack: NDVI vigour signals, electrical-conductivity soil maps and terroir mapping
Read the rings · cross-section
Toolkit
a tree keeps its history in its rings. this one keeps my stack: the foundations at the core, each layer I build on growing outward to the bark.
Two base camps · Pune → Vancouver
Education
every climb starts somewhere. mine started in pune, and picked up again in vancouver.
Base camp 01 · Pune, IndiaElev. 560 m
2020 — 2024 · 18.52°N 73.86°E
B.Tech, Computer Science
Specialisation in Data Science
Symbiosis Institute of Technology
Four years of computer science with a data science specialisation. Python stopped being homework here and became the first tool I reach for.
Summit log
- B.Tech research: asteroid hazard classifier on 958K NASA records
- NYC Airbnb analysis: geospatial EDA over 50,000+ listings
- Built the foundations: ML, statistics, databases, algorithms
Base camp 02 · Vancouver, CanadaElev. 70 m
2025 — 2026 · 49.26°N 123.25°W
Master of Data Science
4.0 / 4.0 GPA · graduate TA
University of British Columbia
A year of intensive data science: unsupervised learning, NLP, Bayesian statistics and the engineering it takes to ship models, not just train them.
Summit log
- Built the Reddit UBC Reporter at UBC CIC's Fall 2025 GenAI hackathon
- Built DisasterDash, a Shiny dashboard over EM-DAT, for DSCI 532
- Reimplemented MMST-ViT, a vision transformer for crop yield
Send a postcard · ranger station
Let's work together
Post card
Hi there! Hiring for a full-time or contract role, or got a project with a model that needs to actually ship? I'd love to hear about it. Grab a slot on the calendar, or reach me at any of these. I reply within a day, and I'm always honest about what's feasible.
— Ojasv