Data Scientist specialized in machine learning and analytics to solve business problems through data-driven decisions. Focused on delivering clear, actionable insights that generate measurable business impact.
<p>I led data science projects from start to finish, from business problem definition to the delivery of actionable insights and predictive models.</p><p></p><p>I designed, built, and validated regression, classification, and recommendation system models using Python (TensorFlow, Keras) and R.</p><p>I used advanced SQL to extract, clean, and prepare large volumes of data, ensuring quality for modeling.</p><p></p><p>I translated model performance into measurable business impact, including projecting a 34% increase in web traffic and achieving an RMSE of 0.783 in a recommendation engine.</p><p></p>
<p>Worked on predictive modeling, passenger analytics, and operational reporting at a major international airport.<br>
Focused on flight punctuality, customer experience, and standardized data pipelines across departments.<br>
Built a flight delay risk model that, with only 30% alert coverage, identified 72% of delayed flights, improving on-time performance by +4.3pp on targeted flights in 12 weeks.<br>
Linked 4,200+ passenger surveys with operational KPIs; satisfaction scores increased from 3.6 to 4.1, while formal complaints per flight decreased –10% after targeted improvements.<br>
Developed SQL data marts, R pipelines, and Tableau dashboards updated hourly, reducing manual reporting time by 80% and standardizing KPIs (punctuality, baggage, gate changes, cancellations, security wait times).<br>
Delivered three recurring reports with measurable results: Weekly Operations (–12% gate changes), Passenger Experience (–9% peak-hour queues), and Baggage & Punctuality (–8% incidents).</p>
Built a flight delay risk model that identified 72% of delayed flights, improving on-time performance by +4.3pp, Linked 4,200+ passenger surveys with operational KPIs; satisfaction scores increased from 3.6 to 4.1, Developed SQL data marts, R pipelines, and Tableau dashboards updated hourly, reducing manual reporting time by 80%, Delivered three recurring reports with measurable results: Weekly Operations, Passenger Experience, and Baggage & Punctuality
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