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I turn messy data into systems that are auditable, not a black box.
Computer Engineer and Data Analytics Engineering MS candidate at Northeastern University (GPA 3.94/4.0). I build backend services, REST APIs, and data pipelines, with a specialization in Machine Learning & GenAI systems.
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About
I'm a Data Analytics Engineering graduate student at Northeastern University with a Computer Engineering background from Thapar Institute of Engineering & Technology. I build software end to end — REST APIs, data pipelines, and ML/GenAI systems — grounded in a strong CS foundation of OOP, data structures, and algorithms.
My experience as a Data Analyst & Strategist at Chaitanya Charitable Trust involved owning data end to end for community programs: pulling and joining data with SQL, cleaning messy survey inputs, and turning them into reporting dashboards leadership actually relied on. Today I'm focused on backend engineering, MLOps, and applying LLMs to production systems.
Outside of coursework, I'm building an end-to-end MLOps platform, exploring NLP architectures from first principles, and looking for full-time roles starting January 2027.
Palak Tanwar — Boston, MA
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Work Experience
One role, in depth — the data programs I owned end to end.
Data Analyst & Strategist
Chaitanya Charitable Trust — Gujarat, India
- Owned data end to end for community programs: pulled and joined data with SQL, cleaned messy survey inputs in Python and spreadsheets, and turned them into trusted reporting dashboards leadership relied on.
- Translated vague, on-the-ground needs from 1,000+ stakeholders into clear data definitions and metrics, then used the analysis to decide which campaigns to run and where to focus, growing audience reach 35%.
- Drove cross-functional buy-in by explaining the reasoning behind the numbers in plain language, lifting program participation 30%.
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Technical Skills
The stack behind the systems — languages, foundations, infrastructure, and ML/GenAI tooling.
Languages
CS Foundations
Backend & Cloud
ML / GenAI & Viz
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Featured Projects
A selection spanning NLP from first principles, production MLOps, and applied LLM systems.
★ 1st Place — 6th MLOps Expo @ Google Cambridge
Foresight ML — Corporate Financial Distress Early Warning System
Apr 2026XGBoost · LSTM/GRU · BigQuery · SHAP · MLOps
- Awarded 1st out of 30 teams, judged on system clarity, production-readiness, deployment pipelines, and engineering depth.
- Built an end-to-end pipeline flagging companies at risk of financial distress 6–12 months ahead, fusing SEC filings (10-K/10-Q) with FRED macro indicators for 10,000+ U.S. public companies.
- Engineered 45+ features in BigQuery and pandas; trained XGBoost and LSTM/GRU models tuned for high recall so few at-risk firms slip through.
- Owned the end-to-end deployment layer — model versioning, inference schema, manifest-based lineage — plus SHAP explainability and cross-sector bias checks, keeping the risk score auditable, not a black box.
LLM-Powered MLOps Platform & NL Query Assistant
Dec 2025FastAPI · Google Gemini · MySQL · Neo4j · Streamlit
- Built a FastAPI layer wrapping a Google Gemini natural-language-to-SQL assistant behind a multistage validation pipeline that enforces read-only access and blocks unsafe queries.
- Designed a hybrid MySQL + Neo4j backend tracking datasets, experiments, Git commit hashes, and model artifacts for fully reproducible lineage.
- Shipped a Streamlit dashboard that uses LLMs to auto-generate plain-English summaries of model performance and KPIs for cross-functional teams.
Sentiment Classification with Classical & Neural NLP Models
Jun 2026Python · PyTorch · GloVe · CNN · NLP
- Implemented perceptron and logistic-regression text classifiers from scratch (no ML libraries), improving test accuracy from 76% to 85% via bigram and custom feature engineering.
- Built a Deep Averaging Network with pretrained GloVe embeddings and a CNN with multi-width convolution filters, reaching 90% test accuracy, diagnosing overfitting from loss curves.
- Re-engineered training to mini-batch on GPU for a 20x speedup with no accuracy loss.
Patient Cohort & Readmission Analytics — MIMIC-III
Feb 2025PCA · t-SNE · K-Means · Clustering
- Worked with real ICU patient records under strict privacy handling to segment patients and predict hospital readmissions, an operational metric that drives healthcare cost and care decisions.
- Reduced high-dimensional clinical data with PCA and t-SNE, then used K-means and hierarchical clustering to surface distinct patient cohorts.
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Get In Touch
Seeking full-time opportunities starting January 2027. Reach out if you have a role, a project, or just want to connect.