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For recruiters 🧑🏽‍💻

Data Science MS at Columbia, working on causal inference, experimentation, and predictive models on healthcare and public-sector data. Comfortable owning a question end to end: framing it, building the model, and saying honestly what it can and cannot support.

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read, then matched against her projects and skills, gaps and all ✦

hiring for:

From the résumé 📄

Education

Columbia University

New York, NY

M.S. Data Science, GPA: 3.9/4.0

Aug 2025 - Expected Dec 2026

Coursework: Deep Learning, Generative AI Systems, Agentic AI, High Performance Computing, Causal Inference, Statistical Inference
Teaching Assistant for Natural Language Processing and AI for Public Policy; Data Science Institute Student Council Member

Vellore Institute of Technology

Vellore, IN

B.Tech. Computer Science and Engineering (Data Science specialisation), GPA: 4.0/4.0

Jul 2019 - May 2023

Ranked 7th out of 200 (Top 4%); Merit Scholarship recipient (2019-2023); Program Representative (2019-2023)
Coursework: Machine Learning, Artificial Intelligence, Natural Language Processing, Image Processing, Social Information Networks

Skills

Programming & Frameworks: Python, SQL, R, C++, PyTorch, TensorFlow, scikit-learn, FastAPI, NumPy, Pandas

LLM & AI Systems: RAG, LangChain, Hugging Face, ChromaDB, FAISS, Multimodal AI, Prompt Engineering, Agent Workflows

Data Engineering & Infrastructure: Databricks, PySpark, MongoDB, MySQL, BigQuery, Redis, Docker, Git, REST APIs

Cloud & MLOps: AWS, Azure DevOps, Weights & Biases, CI/CD, Experiment Tracking, Feature Engineering, Model Monitoring

Work Experience

NYC Administration for Children's Services (ACS)

New York, NY

Predictive Analytics Intern

Jun 2026 - Aug 2026

Built an explainability framework for a child welfare risk model on 900K+ records, transforming black-box predictions into transparent, policy-driven decisions through configurable operating thresholds spanning 25-80% recall (0.799 ROC-AUC, 7.2× base-rate precision).
Designed a three-level reliability audit proving explanations reproducible across evaluation sets (ρ=0.991), then established via noise-control testing that only 15 of 149 features carried real signal, setting a defensible limit on what the agency can claim from any feature ranking.

Novartis

Hyderabad, IN

Technical Analyst Intern

Jan 2023 - Jul 2023

Built predictive and time-series forecasting pipelines on environmental operations data, integrating web-scraped external signals, feature engineering, and temporal trend analysis to support sustainability initiatives targeting a 19% annual reduction in carbon emissions.
Engineered a NLP-driven clinical trial analysis workflow for drug-level sentiment mining, automated summarization, and outcome extraction from unstructured research documents, reducing manual literature review and evidence synthesis effort by 40% per quarter.

Shell

Bengaluru, IN

Software Engineer

Aug 2023 - Jul 2025

Developed machine learning forecasting solutions in Databricks (PySpark, SQL) across 12 business units, reducing forecast error by 23% and enabling $100K+ annual cost optimization decisions.
Built production data pipelines, feature engineering workflows, and 5 Blue Prism RPA bots with logging and retry logic, cutting manual reporting effort by 85% (120+ hours/quarter) and improving SLA compliance from 92% to 99%.

Research Experience at Columbia University

Lamont-Doherty Earth Observatory, McKinley Group

Palisades, NY

Data Scientist - Ocean Carbon Data Valuation

Sep 2026 - Present

Extending a Data Shapley framework valuing individual SOCAT surface ocean fCO_2 observations against a physical model prior rather than an empty baseline, using KNN-Shapley approximation and spatially blocked cross-validation to target where sparse, expensive measurements would most improve the ocean carbon sink reconstructions contributed annually to the Global Carbon Budget.

Irving Medical Center, AI4VS Lab

New York, NY

Computer Vision Engineer - Ophthalmic AI & Eye Tracking

Jul 2026 - Present

Developing gaze-augmented deep learning models for glaucoma diagnosis that fuse clinician eye-tracking with OCT report features, aligning model attention with expert reading behavior to make automated screening for irreversible vision loss auditable enough for clinical use.

Irving Medical Center

New York, NY

AI Engineer - Clinical LLM & Phenotyping

Jan 2026 - Present

Built an extraction system with hybrid de-identification, longitudinal EHR reconstruction, and hallucination-aware validation, converting unstructured notes into 56 cardiac sarcoidosis phenotype variables and replacing months of chart review with a manuscript-ready cohort.

Graduate School of Arts and Sciences

New York, NY

Machine Learning Engineer - Human Rights LLM Evaluation

Jan 2026 - Present

Developed a two-stage retrieval-augmented LLM framework with automated web search and chain-of-thought reasoning scoring 27 defense manufacturers across 9 human-rights dimensions, cutting analyst review effort by 80% with kappa and Krippendorff's alpha validation.

Projects

Colon Cancer Trial Causal Analysis

github.com/rishika1099/Colon-Cancer-Trial-Causal-Analysis | open.substack.com/pub/rishika1099/p/prediction-vs-causation

End-to-end causal inference study of the Moertel (1990) colon cancer trial (n=929), evaluating average and heterogeneous treatment effects, mediation pathways, confounding bias, and external validity through transportability analyses.

Safe Start: Child Welfare Prediction

github.com/rishika1099/Safe-Start-NCANDS-Child-Welfare-Prediction

ML & predictive analytics framework for identifying high-risk child-welfare cases using NCANDS data.

Colorectal Cancer Risk Analysis

github.com/rishika1099/Colorectal-Cancer-Risk-Analysis | rishika1099.shinyapps.io/colorectal_cancer_risk_analysis

Visual analysis of how diet and lifestyle contribute to colorectal cancer risk, built in R/Shiny.

⬇ download the PDF or open it as a page

Selected projects 🌱

6 of 104, the ones that argue for this role ✦

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Colon Cancer Trial Causal Analysis

🩺 Healthcare

🧬 Causal Inference📊 Statistical Modeling

End-to-end causal inference study of the Moertel (1990) colon cancer trial (n=929), evaluating average and heterogeneous treatment effects, mediation pathways, confounding bias, and external validity through transportability analyses.

⭑ Code ✿ Live demo 📰 Article

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Safe Start: Child Welfare Prediction

🏛️ Public Sector🕊️ Human Rights

🧬 Causal Inference🔮 Predictive Analysis🌼 Machine Learning

ML & predictive analytics framework for identifying high-risk child-welfare cases using NCANDS data.

⭑ Code

🥗

Colorectal Cancer Risk Analysis

🩺 Healthcare

📊 Statistical Modeling

Visual analysis of how diet and lifestyle contribute to colorectal cancer risk, built in R/Shiny.

⭑ Code ✿ Live demo

🚗

Car Price Prediction

🏦 Finance

🔮 Predictive Analysis🌼 Machine Learning

Predicts used-car prices (India), 65.6% R² with XGBoost + Optuna, SHAP explainability across 4 ensembles.

⭑ Code

🛍️

Customer Churn Prediction

🍲 Food & Nutrition

🔮 Predictive Analysis🌼 Machine Learning

Predicts repeat-purchase behavior for food-delivery businesses and surfaces key retention drivers.

⭑ Code

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Diabetes Risk Prediction

🩺 Healthcare

🔮 Predictive Analysis🌼 Machine Learning

Gradient Boosting on clinical indicators with SHAP for global and per-patient explanations.

⭑ Code

all 104 projects →

Research 🔬

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Sep 2026 – Present

Ocean Carbon Data Valuation

Columbia Lamont-Doherty Earth Observatory (McKinley Group)

Extended a Data Shapley framework to score ocean measurements, applying statistical modeling to improve reconstruction accuracy of carbon sink estimates.

Machine LearningStatistical Modeling

👁️

Jul 2026 – Present

Ophthalmic AI & Eye Tracking

Columbia AI4VS Lab, Irving Medical Center

Developed gaze-augmented deep learning models for glaucoma diagnosis, focusing on model interpretability and alignment with clinician visual search behavior.

HealthcareComputer VisionDeep Learning

🏥

Jan 2026 – Present

Clinical LLM & Phenotyping

Columbia University, Irving Medical Center

Built a clinical information extraction system converting unstructured EHR notes into structured phenotype variables, ensuring patient privacy and accuracy in data extraction.

HealthcareGenerative AINLP

⚖️

Jan 2026 – Present

Human Rights LLM Evaluation

Columbia University, Graduate School of Arts and Sciences

Developed a retrieval-augmented LLM framework scoring defense manufacturers on human-rights due diligence, quantifying LLM-human agreement for policy-risk assessment validation.

Human RightsLegalGenerative AI

Experience 💼

Jun – Aug 2026

Predictive Analytics Intern

NYC Administration for Children's Services

Built a full explainability framework for a child welfare risk model, turning black-box predictions into transparent decisions through configurable operating thresholds and implementing various model evaluation techniques.

Public SectorHuman RightsMachine LearningPredictive Analysis

2023 – 2025

Software Engineer

Shell, Bengaluru

Developed ML forecasting solutions in Databricks, building predictive financial models and production data pipelines, enabling data-driven decisions across business units.

Predictive AnalysisMachine Learning

Jan – Jul 2023

Technical Analyst Intern

Novartis, Hyderabad

Developed predictive and time-series forecasting models on supply chain and environmental operations data, while also engineering an NLP pipeline for clinical trial documents.

HealthcareNLPPredictive Analysis

📎 1 file inside

Feb – Mar 2022

Data Visualization Intern

Saint Louis University

Analyzed Facebook ad campaign data to surface insights that informed targeting and strategy, improving campaign performance and ROI.

Statistical Modeling

📎 1 file inside

Education 🎓

2025 – present

Master of Science

Data Science

Columbia University, New York

Focused on machine learning, LLM systems, and causal inference, with relevant coursework and research assistant experience on LLM projects.

2019 – 2023

Bachelor of Technology

Computer Science & Data Science

Vellore Institute of Technology (VIT)

Completed coursework in Data Science and Machine Learning, gaining foundational knowledge relevant to data science and predictive analytics.

Teaching 🍎

💬

Fall 2026

Natural Language Processing

Teaching Assistant, COMS 4705

Columbia University, Computer Science, taught by John Hewitt

From tokenization and transformers through RLHF, agents and interpretability.

NLPGenerative AIDeep Learning

🏛️

Spring 2026

Artificial Intelligence for Public Policy

Teaching Assistant

Columbia University, School of International and Public Affairs (SIPA)

AI fundamentals for policy students, and how to turn a policy problem into an AI solution.

Public SectorMachine Learning

Skills 🛠️

Causal inferenceExperimentationA/B testingStatistical modelingPredictive modelingExplainabilityFairness auditingPythonRSQL