Digital Marketing Data Analyst

Navya Bhat

Bellevue, WA *****.****@*****.*** 425-***-**** Linkedin Portfolio

EDUCATION

University Of Texas - Dallas, Dallas, TX Aug 2022 – Dec 2023 Master of Science in Business Analytics - Concentration in Data Science GPA: 3.75/4.0 Ranked under top 10 in all 5 rounds of Stukent's Digital Marketing Simulation Internship Contest Sir M Visvesvaraya Institute of Technology, Bengaluru, India Aug 2015 - Jun 2019 Bachelor of Engineering in Information Science GPA: 3.66/4.0 SKILLS

Programming: Python(PyTorch, scikit-learn), SQL, R, Spark, Hadoop Database Systems : MySQL, Oracle, Spark SQL

Tools/Frameworks: Tableau, Excel, AWS (Redshift,S3, Sagemaker), Airflow, Kafka, Kinesis Competencies: ETL, Data Warehousing, Regression, Ensemble, Decision Trees, NLP, Hypothesis Testing, Deep Learning, Statistics PROFESSIONAL EXPERIENCE

Data Analyst, CrowdDoing Feb 2024 – Jan 2025

● Delivered interactive Tableau dashboards with KPIs and a 4-year brand performance and consumer behavior trend analysis. This initiative led to uncovering $500K in untapped growth opportunities, guiding leadership’s marketing strategies and spending decisions.

● Integrated and standardized semi-structured survey and customer feedback data into a comprehensive analytics framework through deep-dive analysis. Transformed these insights into actionable features for the downstream modeling team. Data Scientist, Temenos ( Explainable AI ) Aug 2020 – Jun 2022

● Developed a high-volume fraud detection prediction model, identifying 75% of fraud cases and uncovering opportunities to strengthen security. Optimized data pipelines to reduce memory usage by 30% and boost computational performance by 40%.

● Spearheaded A/B testing using significance testing and power analysis to rigorously compare model enhancements. Leveraged causal inference to isolate key drivers and drive a 20% boost in predictive accuracy, fueling business growth through optimized fraud detection.

● Enhanced customer retention by applying advanced classification and cohort analysis techniques to develop a retention model that improved retention rates by 20% and integrated model insights into robust warehousing workflows.

● Automated data profiling and quality checks, reducing manual effort by 30%. Orchestrated scalable workflows and advanced analysis techniques to enhance data accuracy and integrity through cross-functional collaboration. Software Developer, Temenos (Triple-A) Jul 2019 - Jul 2020

● Documented business objectives and technical requirements in the Wealthsuite application, collaborating with cross-functional teams to enhance usability. Improved product adoption, reducing churn rate by 47%.

● Optimized APIs and UI components (Spring Boot, React.js) to cut loading times by 70%, ensuring a seamless user experience and illustrating a strong focus on product development.

● Streamlined DevOps pipelines by designing and implementing CI/CD automation scripts, reducing manual intervention by 90% and enhancing live production support for feature deployment. Data Scientist Intern, iTAS Innovations Dec 2018 - Jun 2019

● Implemented scalable data ingestion and preprocessing of high-volume streaming data from IoT Bluetooth modules, improving data integrity by 30%. Supported real-time analytics for predictive maintenance through advanced statistical methods and machine learning.

● Designed and developed data workflows to process real-time sensor data in Building Automation Systems, cutting data latency by 75% and delivering actionable insights to stakeholders for continuous improvement. ACADEMIC PROJECTS

Outpatient Care Analysis (Python, Machine Learning) Oct 2024 – Nov 2024

● Extracted and transformed large-scale healthcare claims and prescription data to develop patient-level outpatient history reports, supporting cross-functional stakeholder decision-making.

● Engineered structured datasets and performed EDA to identify utilization patterns, care gaps, and health trends.

● Built interactive visualizations using Python libraries to communicate data-driven insights, helping optimize outpatient care utilization and reduce care gaps by 32%.

Chest X-ray Image Classification (Unstructured Data, Computer Vision, PyTorch) June 2024 – Jul 2024

● Cleaned and preprocessed a large medical imaging dataset to train a deep learning model in PyTorch, achieving a 93.75% validation accuracy for pneumonia detection.

● Utilized transfer learning and fine-tuning techniques on the ResNet50 architecture, documenting the modeling approach, data gathering procedures, and analytical metrics for reproducibility. Loan Default Risk Analysis (Scikit Learn, Pandas, NumPy ) Mar 2023 – Apr 2023

● Analyzed 800,000+ loan records, performing EDA and feature engineering to improve predictive accuracy for high-risk loans.

● Developed statistical and ML models (Logistic Regression, GLMs, ensemble methods) with regularization, delivering data-driven insights to stakeholders and accelerating continuous improvement of the loan assessment process.

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