Barcelona, Catalonia, Spain
As the head of science and analytics in the EU Prime & Marketing Org, I manage a team of 20 scientists, economists, business intelligence engineers, and data engineers working across a wide range of customer problems. My team works across a large domain of problems: i) Developing Bayesian Geo-RCT models for marketing measurement, b) Building attribution-methods for evergreen marketing measurement, c) Building GenAI-based products to facilitate data analytics by non-technical, d) Recommendation systems and causal inferencing for Prime-exclusive promotions, e) Modeling the business impact of speed changes in the Amazon delivery network, f) Econometric models to model impact of Prime benefits, g) Data analytics for SVP-level business reviews, i) Building the suite of all analytic products of Prime & Marketing in EU (200+ PMs), and supporting them with data-drive business decision. My team has published packages in causal inferencing (e.g. https://github.com/amazon-science/causal-validation), and has authored several scientific papers. I am also the developer of optidef, a Latex library for defining optimization problems, and of the epftoolbox python library, an open-access library for driving research in electricity price forecasting. You can find more about me and my current research in my personal site linked below.
As the head of science and analytics in the EU Prime & Marketing Org, I manage a team of 20 scientists, economists, business intelligence engineers, and data engineers working across a wide range of customer problems. My team works across a large domain of problems: i) Developing Bayesian Geo-RCT models for marketing measurement, b) Building attribution-methods for evergreen marketing measurement, c) Building GenAI-based products to facilitate data analytics by non-technical, d) Recommendation systems and causal inferencing for Prime-exclusive promotions, e) Modeling the business impact of speed changes in the Amazon delivery network, f) Econometric models to model impact of Prime benefits, g) Data analytics for SVP-level business reviews, i) Building the suite of all analytic products of Prime & Marketing in EU (200+ PMs), and supporting them with data-drive business decision. My team has published packages in causal inferencing (e.g. https://github.com/amazon-science/causal-validation), and has authored several scientific papers.
Science manager of the ML team in the EU Prime & Marketing Tech organization. I managed a team of 6 applied scientists and acted as the science lead of organization. My team worked across a large domain of problems: i) measuring customer engagement via hidden Markov models; ii) measuring marketing impact of advertising in video marketing campaigns; iii) evaluating downstream valuation of discounts via causal inference.
Science Lead of the EU Prime & Marketing Org. Worked across a variety of science problems across a team of scientists, engineers, and BIEs. Projects: Building and designing recommendation algorithms for next action prediction. Building forecasting models for deals recommendations. Measurement of marketing channels via causal inferencing and experimentation.
Development of machine learning, data analysis, and optimization algorithms that facilitate the energy transition and the integration of renewable sources.
Development of machine learning, data analysis, and optimization algorithms that facilitate the energy transition and the integration of renewable sources.
- Controlling a tethered kite for energy generation in the field of airborne wind energy (AWE). In particular, implementation of a NMPC scheme in order to track periodic optimal trajectories in real flight conditions. - Modelling and solving periodic optimal control problems to generate flight trajectories that maximize the extracted energy. - Developing a Latex library for defining optimization problems and designing exercises for a book on Numerical Optimal Control. - Development of high frequency readout system for 3 biomedical sensors used for different blood-related measurements.
- Lecturer assistant in the lecture of Modelling and System Identification. Main tasks: creating and tutoring exercise sessions, creating and grading exams and supervising students. - Tutor in the exercises sessions of the lecture of Micro-mechanics of the Master of Microsystems. Main tasks: solving doubts, guiding students with the course issues and grading exams.