Hi, I'm Jaime

I am a highly adaptable Data Scientist with a strong passion for data analysis and artificial intelligence. My ability to learn quickly enables me to absorb new technologies and methodologies rapidly, allowing me to apply these skills effectively to solve complex problems. My focus on problem-solving drives me to critically analyze situations and develop innovative, data-driven solutions.

I naturally thrive in collaborative environments valuing the ideas and input of others to achieve shared goals. I communicate clearly and listen attentively to my team, which helps in building strong and effective relationships in any setting. My technical expertise is complemented by my ability to work cohesively within a team.

Overall, I consider myself a highly capable professional eager to tackle any challenge that comes my way. My strong learning capabilities, problem-solving skills, adaptability, teamwork and effective communication make me a valuable asset in any environment. I am committed to continuously developing these skills to grow both personally and professionally.

I am a Mathematics and Engineering student with experience in the field of data analysis and AI consulting. I have worked on projects involving data collection and data cleaning, as well as developing predictive models and generative AI systems. In my current role as a Data Scientist and AI Consultant at KPMG, I developed and implemented a generative AI system using OpenAI for internal queries, significantly enhancing decision-making efficiency. I have also led efforts to optimize AI models through techniques such as prompt engineering and embeddings.

During my time as a Business Analyst at Neovantas, I managed and analyzed large volumes of complex databases to extract key insights and generate detailed reports. Additionally, I developed forecasting models to predict customer satisfaction, and used tools like Python and Power BI to create interactive visualizations, facilitating data-driven decision-making.

I am constantly learning new techniques and tools, such as SQL and Power BI, to enhance my skills and provide effective solutions to business challenges. I am excited to continue my career in the field of data analysis and artificial intelligence, and I am looking for opportunities to develop and collaborate with innovative teams in this field.

As a soccer coach with a passion for data analysis, I have always been fascinated by the wealth of information hidden within sports statistics. Whether it's analyzing goals scored in soccer, shots taken in padel, or measuring golf swing metrics, data analysis provides valuable insights for improvement.

In my coaching role, I utilize data analysis tools to gain insights into team performance. By analyzing metrics like goals, passes completed, and shots taken, I can identify patterns and develop strategies to enhance the team's game. Similarly, in padel and golf, I use data analysis to track performance, examining factors like shots, accuracy, and distances to improve my own skills.

Overall, as a sports coach, data analysis plays a pivotal role in understanding and enhancing performance across various sports, allowing me to make data-driven decisions to improve both individual and team outcomes.

Client Analysis Mapfre, Verti BTS and Santander Bank

1) Design a predicting model for customer satisfaction of the clients of Banco Santander Mexico from the different recording calls.

3) Design a predicting model to predict the customers that were going to renew their insurance policy of the company Verti (Insurance company).

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World Cup Analysis

The project focuses on analyzing the relationship between the formations used and the average age of the players of the teams participating in the World Cup, and their impact on the results of the matches. To carry out this analysis, information was collected from the teams participating in the last five World Cups, including the formations used and the average age of the players.

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Nashville's housing data

The project involves cleaning and standardizing Nashville's housing data. It includes converting date formats, populating missing address data, breaking out address and owner details into individual columns, and standardizing fields. The project culminates in the removal of unused columns to prepare the data for analysis.

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