• Enabled targeting of high-potential app users by integrating PySpark machine learning models with CRM data through SQL pipelines on Apache Airflow.
• Increased marketing spend efficiency by conceiving high-ROI initiatives through ideation and A/B testing process, leveraging Amplitude reports and agile methodologies.
• Proposed and implemented an experimentation framework for A/B testing best practices.
• Trained predictive machine learning models using AWS cloud infrastructure (SageMaker, Athena and Glue) and PySpark.
• Assessed ROI of referral campaigns via advanced causal inference techniques when A/B Testing was not feasible.
• Developed user-friendly dashboards on AWS QuickSight to enable C-level executives to conduct simulations and what-if analysis for promotional campaigns on our app home screen.

• Guided deployment of new network infrastructure, identifying areas where upgrades would reduce churn the most.
• Tracked network deployment results via geospatial dashboards blending network KQIs with churn and NPS metrics.
• Boosted conversion of telesales by developing purchase propensity predictive models with LightGBM.

• Developed data transformation processes and implemented machine learning models that allowed the deployment of a personalized marketing strategy for the company’s most important local brand. This included the development of churn models and clustering on transactional data.
• Led the development of an internal R package aimed at streamlining processes and accelerating deliveries in our area (find out more here).
• Led the impact evaluation of the personalised marketing project by designing, implementing, and supervising A/B tests, in close collaboration with the CRM Ops team. This included devising solutions for increasing statistical power in contexts of small treatment effects.
• Introduced the use of version control and unit tests to the team.

• Provided data-driven insights to marketing and merchants, leveraging econometrics and clear storytelling.
• Automated complex, ad-hoc reports through R Markdown and Tableau, increasing throughput of our team.
• Assessed the impact of key, C-level decisions via causal inference techniques such as Difference in Differences.

A one-year post-graduate program blending computer science, mathematics, and business to develop AI solutions. Cumulative GPA: 3.93.

Coursework includes:

• Machine Learning I and II: usage of sci-kit learn, openCV (computer vision), NLP and audio-processing libraries.
• Data Visualization Techniques: advanced use of Tableau for reporting and data storytelling.
• Big Data Techniques: Azure Fundamentals, Hadoop ecosystem (HDFS, Pig, Hive, Beeline), Spark, and ETL processes with SQL Server Integration Services.
• Foundations of Data Management: Data Management Body of Knowledge framework.
• Applied Mathematical Concepts for Machine Learning: linear algebra, matrix factorization, TF-IDF, gradient descent, convolution, Fourier transforms, signal processing.
• Design Thinking for AI.