Senior Data Scientist - Exl
As a Senior Data Scientist, I architected advanced pricing optimization strategies using decision trees, neural networks, and regression models, enhancing model precision by 12% with XGBoost and streamlining data warehousing performance tracking by 30%. By reducing SMAPE to 26.8 through strategic clustering, data manipulation, and comprehensive exploratory data analysis (EDA)—including feature importance, covariance matrices, collinearity tests, and VIF scores—I delivered more accurate price optimization solutions. Additionally, I automated the pricing process for a fashion retail client, leveraging predictive modeling for seasonality, market trends, and product attributes, which eliminated manual updates and reduced operational efforts by 40%.
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Data Scientist - Uber
At Uber, I devised a strategy utilizing the TF-IDF Natural Language Processing (NLP) algorithm to compare restaurant pricing on Uber-Eats against competitors, identifying 45% of listings with discrepancies exceeding 20%. I also leveraged NLP algorithms to analyze user feedback, leading to an enhancement of the Net Promoter Score (NPS) by an average of 0.7 points across multiple geographies through the identification and application of strategic insights. Additionally, I predicted driver demand using time series forecasting, which optimized driver allocation efficiency by 15% and increased customer satisfaction by 20% through accurate demand predictions in specific regions and new geographies.
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Business Intelligence Engineer - Amazon
In my role, I led the development and fine-tuning of Transformer-based models using PyTorch and TensorFlow for large-scale demand forecasting, resulting in a 25% improvement in forecast accuracy and a 30% reduction in stock management issues. I also spearheaded the launch of a distributed data pipeline utilizing Apache Spark, which reduced data processing time by 50% and enabled near real-time analytics for e-commerce operations. Additionally, I innovated and deployed a Generative Adversarial Network (GAN) for generating synthetic training data, increasing data diversity by 30% and boosting model performance metrics by 15%.
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Data Science Analyst - United Airlines
I developed a collaborative-based recommendation engine for united.com, utilizing advanced modeling techniques, which led to an incremental revenue increase of $2 million monthly. By analyzing customer purchase data through data mining and A/B testing, I engineered actionable insights that boosted conversion rates by 15% and increased retention by 10%, significantly enhancing the overall customer experience. Additionally, I implemented a balanced approach to price-sensitive and personalized recommendations using pattern recognition techniques, resulting in a 12% increase in recommendation take rates.
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