Data Science Information Technology

Catherine Smereena Dommaty

**** ************ ******, ******, **

*****************.*******@*****.***,+1-214-***-****, https://www.linkedin.com/in/catherine-smereena-dommaty-59036828b/,

https://github.com/catherinesmereena .

PROFESSIONAL SUMMARY

Data Science and Analytics graduate with hands-on experience in building scalable ML solutions for search, ranking, and query

understanding. Proficient in Python, SQL, and cloud platforms with a strong focus on NLP and semantic retrieval. Skilled in

embedding models, anomaly detection, and delivering data-driven strategies that enhance user experience and business performance.

EDUCATION

Northeastern University Boston, MA

Master of Professional Studies Analytics, Statistical Analytics. GPA: 3.8

Coursework: Data Mining, Data Modelling, Database Administration, Predictive Modelling, Python, Big Data Technologies,

SQL, Data Warehousing, Risk Management, Data Visualization, Healthcare Pharmaceutical Analytics

St. Joseph's Degree & PG College Hyderabad, Telangana

Bachelor of Commerce, Information Technology. GPA: 4.0

Coursework: Python, Data Modelling, Database Management, Statistics, Accounting, Business Tax Law

PROJECTS

Franchise Growth Analytics & Lead Optimization

Built a lead scoring model to optimize conversions by analyzing account activity and engagement. Applied statistical tests (t-test,

chi-square, Spearman) to identify success drivers. Developed real-time dashboards in Tableau, standardized datasets to reduce

inconsistencies by 60%, and automated SQL pipelines to enhance reporting and operational decision-making.

NFL & Housing Data-Predictive Statistical Analysis

Built play-type classifiers (KNN, Logistic, Random Forest) (Pandas, NumPy, SciPy), boosting prediction accuracy to 73% using

engineered features like Down Importance and Time Pressure.

Applied stepwise regression on housing data, identifying square meters and cityPartRange as key predictors (R = 0.754).

Refined feature quality using VIF, IQR-based capping, and SMOTE to reduce noise and balance target classes.

Designed modular ML pipelines for EDA and model tuning in Python (Pandas, Scikit-learn, Stats models).

Customer Churn Prediction Model

Built classification models (Logistic Regression, Random Forest) with an 85% accuracy rate to predict churn risk.

Conducted A/B testing & experimental design to refine retention strategies based on data-driven insights.

Developed a Power BI dashboard for leadership to track real-time churn trends.

Netflix Content Analytics & Recommendation Optimization

Analyzed 8,800+ Netflix titles using R and Python to uncover trends in genre, ratings, and viewer behavior. Applied clustering and

association rule mining (Apriori, Eclat) to improve content recommendations. Conducted EDA and modeling to guide strategic

decisions in content acquisition and enhance user engagement.

Excel-Based Business Analytics

Developed advanced Excel models to solve business problems in inventory management, profitability optimization, and financial

forecasting. Key projects included EOQ and Monte Carlo simulations, linear programming for profit maximization, and stock price

forecasting for NFLX and AMZN. Performed cost-benefit analysis and content segmentation using association rule mining. Leveraged

Solver, What-If Analysis, and MAPE for validation. Delivered actionable insights on pricing, resource planning, and expansion.

SKILLS

Programming: Python, R, SQL Optimization Techniques: Linear Programming, Gradient

Data Science: Visualization, Machine Learning, Predictive Descent, Hyperparameter Tuning

Analytics, Statistical Modeling, Data Cleaning, Feature Model Deployment: Flask

Engineering Business Intelligence: Data Visualization Tools, Business

Visualization: Tableau, Power BI, Matplotlib, Seaborn Intelligence Reporting, Dashboarding

Databases: MySQL, PostgreSQL Additional Tools: NLP& Query Understanding

Big Data: Hadoop, Spark

CERTIFICATIONS

Data Science Professional Certificate (2024): Hands-on experience in machine learning, deep learning, and AI frameworks.

Python for Data Science Certification: Proficiency in NumPy, Pandas, Scikit-learn, TensorFlow.

SQL Programming Certification: Expertise in complex queries, joins, aggregations, and stored procedures.

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