+ Defining the data strategy for the organization, and identifying opportunities to leverage data science to drive business insights and value.+ Leading the implementation of the company's novel commercial model, which aims to prioritize Sales Force Effectiveness (SFE) and Customer Experience (CX) as the primary catalysts for this transformation.+ Managing the budget for the data team, and making strategic decisions about resource allocation and investments in new technologies and tools.+ Leading a team of data scientists and overseeing their work, including project planning, prioritization, and execution.

+ Boost of data science's growth inside the company by creating and leading an analytics team and spreading the data culture.+ Leading the implementation of strategic commercial projects that contribute to the digitalization and optimization of processes, impacting the Sales Force Effectiveness (SFE) and Customer Experience (CX) of the company.+ Guaranteeing the proper choice and use of technological frameworks that help build AI solutions.+

Novartis is a Swiss multinational pharmaceutical company based in Basel, Switzerland. Among the tasks I contributed to are: + Apply Machine Learning techniques to optimize financial processes. + Contribute to the implementation of the “Data Culture” in the company.

Banc de Sabadell is a Catalan multinational financial services company headquartered in Alacant and Barcelona. It is the fourth-largest Spanish banking group. Among the tasks I contributed to were: + Implement Machine Learning models and algorithms with the objective to gain insights from the costumers. This knowledge can be leveraged to give a better service by anticipating clients' needs. + Develop a Machine Learning model to predict economic recession periods in the long term horizon for the biggest economies in the World. + Stablish an approach to Data Ethics involving bias mitigation and explainability of ML models; data tends to have intrinsic biases which can be difficult to find at a first glance. Those biases can be carried throughout all the dataflow; thus, the ML algorithm can take them as absolute truth and use them in its predictions. That may lead to having a model which is simply wrong or, worst case scenario, a model which is racist or sexist, among other discriminations. That is why mitigating biases and being able to understand and interpret how the model is taking its decisions internally is a key point in the process of data modelling.

Annalect is the data and technology expert branch of Omnicom Group Inc., an American global media, marketing and corporate communications holding company headquartered in New York City. Among the tasks I contributed to were: + Model main KPIs that drive business decision making. That allows business to understand their main drivers and optimize future resources and strategies. + Model the contributions of the different actions taken in the path of costumer conversion. That allows business to understand the customer’s journey and contribute to the optimization of future marketing campaigns. + Report main KPIs and business drivers into a dashboard, making the current state of business easier to understand at first glance.