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Rishika Mamidibathula

Data Scientist & ML Engineer · New York City

rm4318@columbia.eduin/rishika-mamidibathulagithub.com/rishika1099rishika1099.substack.com

Experience

Data Science Intern

Summer 2026

NYC Administration for Children's Services

Predictive risk models on child-welfare data with explainable ML, fairness auditing, and causal adjustment for high-stakes public-sector decisions.

  • Explainable ML on sensitive child-welfare data.
  • Fairness auditing baked into every model.
  • Causal adjustment for high-stakes public-sector decisions.

Software Engineer

2023 – 2025

Shell, Bengaluru

Built and deployed machine-learning forecasting pipelines in Databricks across 12 business units.

  • Designed and shipped ML forecasting models in Databricks across 12 business units, cutting forecast error by 23%.
  • Drove over $100K in operational savings through improved demand forecasting and process automation.
  • Built RPA bots that automated recurring reporting, cutting manual effort by 85%.
  • Partnered with business stakeholders to translate forecasts into planning and resource decisions.

Technical Analyst Intern

Jan – Jul 2023

Novartis, Hyderabad

Built NLP and time-series workflows supporting clinical-trial analysis and sustainability goals.

  • Developed an NLP workflow to mine and summarize sentiment from clinical-trial text at scale.
  • Built time-series pipelines that informed operations toward a 19% carbon-reduction goal.
  • Delivered analyses that fed into cross-functional decision-making.

Data Visualization Intern

Feb – Mar 2022

Saint Louis University

Built Tableau dashboards to analyze campaign performance and guide resource allocation.

  • Designed Tableau dashboards tracking campaign-performance metrics across channels.
  • Surfaced insights that sharpened analysis and guided how resources were allocated.

Research Assistant: Clinical LLM & Phenotyping

Jan 2026 – Present

Columbia University Irving Medical Center

An LLM pipeline that turns years of messy clinical notes into structured, research-ready data, with patient privacy and accuracy built in.

  • Built an end-to-end system that reads years of cardiology and rheumatology notes for a cohort of cardiac-sarcoidosis patients and extracts dozens of structured clinical variables.
  • Reconstructed fragmented hospital records into clean, chronological patient timelines so the model could reason over how the disease and treatments evolved.
  • Designed a HIPAA-safe de-identification step that strips out patient identifiers before anything reaches the model, with no protected data ever written to disk.
  • Engineered safeguards so the model extracts only explicitly stated facts, without inferring or imputing missing values.
  • Validated the extracted data against blinded chart review by two clinicians to measure real-world accuracy.

Research Assistant: Human Rights LLM Evaluation

Jan 2026 – Present

Columbia GSAS

An LLM framework that scores defense manufacturers on human-rights due diligence and checks its own judgments against expert raters.

  • Automated human-rights due-diligence scoring for 27 defense manufacturers, grounded in UN, UNICEF, and Arms Trade Treaty frameworks.
  • Scored each company across nine dimensions, including a dedicated set of children's-rights criteria.
  • Designed a two-stage, evidence-grounded pipeline: the first stage retrieves and quotes source text from company policy documents, and the second scores it with transparent, auditable reasoning.
  • Benchmarked the model's scores against expert human raters and reported how closely they agreed.
  • Produced an auditable report where every score traces back to its source.

Education

M.S. in Data Science

2025 – present

Columbia University, New York

GPA 3.87, focus on machine learning, LLM systems, and causal inference.

  • Coursework: Applied Deep Learning, LLM-based Generative AI Systems, Causal Inference, High Performance Machine Learning, Machine Learning, Statistical Inference and Modelling, Exploratory Data Analysis and Visualization, and Agentic AI.
  • Teaching Assistant for Artificial Intelligence for Public Policy at the Data Science Institute.
  • DSI Student Council, Communications & Professional Resources.
  • Research assistant on two LLM projects: clinical phenotyping and human-rights evaluation.

B.Tech, Computer Science & Data Science

2019 – 2023

Vellore Institute of Technology (VIT)

4.0/4.0 GPA · graduated ranked 7th of ~200 (top 4%).

  • Merit Scholarship recipient and Program Representative, 2019 to 2023.
  • Data Science coursework: Artificial Intelligence, Machine Learning, Deep Learning, Natural Language Processing, Image Processing, Predictive Analytics, Business Intelligence and Analytics, and Social and Information Networks.
  • Computer Science coursework: Data Structures and Algorithms, Object-Oriented Programming, Database Management Systems, Operating Systems, Computer Architecture, Theory of Computation and Compiler Design, Network and Communication, Internet Programming and Web Technologies, Internet of Things, and Cryptography and Network Security.
  • Mathematics coursework: Calculus, Applied Linear Algebra, Discrete Mathematics and Graph Theory, Statistics, and Differential Equations.

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