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Ananya Deshpande

Data Scientist (Entry-Level)

Pune, India • +91 98220 51764 • ananya.deshpande.ds@gmail.com • linkedin.com/in/ananya-deshpande-ds • github.com/ananyadesh

Portfolio: www.eliorexa.com/portfolio/data-scientist-fresher

Professional Summary

Final-year Statistics & Computer Science graduate who turns messy data into clear decisions using Python, R and SQL. Comfortable across the workflow — exploratory analysis in pandas, hypothesis testing, machine-learning models in scikit-learn, and crisp data visualization in Tableau. Analytically curious and rigorous about statistics, demonstrated across a forecasting capstone, a churn-prediction project and a Kaggle medal.

Skills

Languages: Python, R, SQL, Bash

ML & stats: scikit-learn, statistics, machine learning, hypothesis testing, A/B testing, regression, time-series

Data wrangling: pandas, NumPy, dplyr, Jupyter, feature engineering

Visualization & BI: Tableau, matplotlib, seaborn, ggplot2, Power BI

Core Competencies

Python · R · SQL · statistics · machine learning · pandas · scikit-learn · data visualization · A/B testing · Tableau · Data Scientist

Work Experience

Data Science Intern — Lumio Analytics

May 2025 – Aug 2025

Remote

  • Built a churn-prediction model in scikit-learn (gradient boosting) reaching 0.88 ROC-AUC, flagging at-risk accounts 3 weeks earlier than the existing rules engine.
  • Automated a weekly KPI pipeline in Python + SQL that replaced a manual spreadsheet, saving the analytics team ~6 hours/week.
  • Ran an A/B test on two onboarding emails (n = 18k), establishing statistical significance (p = 0.013) and a 9% lift in activation.
  • Designed 4 Tableau dashboards for the growth team, cutting recurring ad-hoc data requests roughly 40%.

Projects

DemandCast — retail demand forecasting (capstone) — MAPE 19% → 11%

Time-series forecasting system for a mid-size grocery chain, built as the degree capstone.

  • Engineered features from 3 years of sales data in pandas and benchmarked SARIMA against gradient-boosted trees, cutting forecast MAPE from 19% to 11%.
  • Shipped an interactive Tableau dashboard so category managers could explore forecasts by store and SKU.

Tech: Python, time-series, scikit-learn, Tableau

FraudLens — transaction anomaly detection — 0.91 fraud recall

Imbalanced-classification study on a public payments dataset.

  • Handled severe class imbalance with SMOTE and cost-sensitive learning, lifting recall on fraud cases to 0.91 at fixed precision.
  • Documented the statistical evaluation (precision-recall, calibration) in a reproducible R Markdown report.

Tech: Python, R, machine learning, statistics

Education

B.Sc. (Hons.) Statistics & Computer Science

2026

Fergusson College (Savitribai Phule Pune University), Pune

CGPA 9.1/10 · Coursework: Probability, Inference, Regression, Machine Learning, Databases, Data Visualization

Certifications

  • IBM Data Science (Professional Certificate) — Coursera (2025)
  • Tableau Desktop Specialist — Tableau (2025)