Senior Data Scientist with 11+ Years Experience

Reema Naik Desai

408-***-**** **********.*****@*****.***

Summary

Data Scientist with over 11 years of experience building robust analytical workflows and deploying machine learning models in production. Skilled in Python (Pandas, NumPy), SQL, and deep learning frameworks (TensorFlow, PyTorch) to analyze large-scale datasets and extract actionable insights. Proven ability to create interactive dashboards and optimize predictive models to drive strategic decision-making.

Professional Experience

Heartland Bank

Dec 2021 - Present

Data Scientist

•Create dashboards and reports to monitor key KPIs enabling data-driven decisions by senior leadership.

Dallas, TX

•Utilize Python and SQL to extract, clean, and analyze large datasets for regulatory and strategic reporting, uncovering insights through rigorous data analysis.

•Leverage machine learning techniques such as regression modeling, anomaly detection, and predictive analytics to uncover actionable business insights, optimize decision-making, and enhance data-driven strategies with reproducible analytical workflows.

•Conduct A/B testing on marketing campaigns to evaluate effectiveness and optimize customer acquisition strategies.

AT&T Mobility Dec 2012 - Nov 2020

Data Scientist Dallas, TX

•Analyze large-scale datasets, uncovering actionable trends and correlations that influenced strategic decisions and improved operational efficiency.

•Designed and maintained dynamic dashboards and reproducible analytical workflows to enable real-time visualization of critical business metrics, leading to faster decision-making and increased stakeholder engagement.

•Led data cleaning, preprocessing, and feature engineering on large-scale datasets, transforming raw data into high-quality inputs and improving model accuracy through applied AI research.

•Used Python libraries like Pandas and NumPy to perform data cleaning, manipulation, and analysis on large datasets, resulting in increase in reporting efficiency.

•Conducted in-depth Exploratory Data Analysis (EDA) on data to identify key factors influencing service quality and customer behavior.

•Experience applying AI research and machine learning models-such as XGBoost, deep learning frameworks, and clustering-for production-ready analytical systems, ensuring validation and optimization of measurement methods.

•Familiar with data loading and transformation processes using Snowflake, ensuring data pipelines were efficient, reliable, and maintained data governance standards.

•Effectively communicated complex data insights and actionable recommendations in English to cross-functional teams, bridging the gap between technical and non-technical stakeholders and driving end-to-end ownership of solutions.

•Collaborated with cross-functional teams to translate data-driven insights into strategic business decisions

•Network expertise, with a solid understanding of 4G/LTE/VoLTE/5G wireless technologies.

Education & Certifications

University of Texas at Austin, McCombs School of Business, USA

2024

Post-Graduate, AI & ML – Business Applications

San Diego State University, USA

2011

Master's, Electrical & Computer Engineering

Goa University, India

2005

Bachelor's, Electrical & Electronics Engineering

Technical Skills

•Programming: Python, SQL

•ML & AI: Gen AI, LLMs, KMeans, bagging and boosting methods, Regression, Classification, KNN, PCA, Clustering, Decision Trees, Random Forest, NLP, Computer Vision, Deep Learning, recommendation system, Machine Learning, Applied AI Research, Intellectual Curiosity

•Frameworks: TensorFlow, PyTorch, Keras, scikit-learn, scipy

•Big Data & Cloud: Snowflake, AWS Sagemaker, H2O.ai, Large-scale Datasets

•Visualization: Matplotlib, Seaborn, Power BI, Excel, Tableau

•Statistics: A/B Testing, Hypothesis Testing, Data Analysis

•Cloud & Distributed Computing: AWS – Sagemaker

•Languages: Russian Language, English Language

Decision Tree Model for Personal Loan Targeting

Academic

•To understand which customer attributes are most significant in driving purchases, and to identify which segment of customers to target more using Decision tree.

•Model Performance Evaluation and Improvement using pre-pruning (using GridsearchCV) and post-pruning.

Optimizing Credit Card Retention with Advanced Machine Learning Models

Academic

•Model that will help the bank improve its services so that customers do not renounce their credit cards using 5 models using decision trees, bagging, and boosting methods.

Support Ticket Categorization using Gen AI

Academic

•Developed a Generative AI application using a Large Language Model (LLM) to automate the classification and processing of support tickets.

•The application predicts ticket categories, assign priority, suggest estimated resolution time and stores the results in a structured dataframe.

Predicting Customer Churn with Neural Networks

Academic

•Built a neural network -based classifier that can determine whether bank customer will leave or not in the next 6 months with Adam optimizer and Dropout, SMOTE and SGD optimizer.

Academic Projects

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