Director of Data Science and AI

As the Senior Manager of Data Science & Artificial Intelligence (AI), I have led a team of 10+ engineers focused on applying AI in the core products of the company. By managing this team, I have played a key role in driving AI implementation throughout the organization.

– Successfully built and expanded the data science team, increasing the number of engineers by 70% through strategic profiling and streamlined selection processes.
– Developed and executed the department’s roadmap, aligning it with team members and stakeholders, including OKR & KPI planning, performance evaluations, and defining departmental functions and tribes.
– Led coordination and supervision of data science technologies for core product and feature implementation, resulting in a 15% cost reduction, 5% increase in model performance, and 10% overall improvement in precision and results.
– Facilitated department redefinition, distributing tribes and clarifying functions to enhance efficiency.
– Promoted data science education through the creation of the Data Science Guild, facilitating knowledge sharing and awareness of implemented technologies.

*Projects:*
Visible and Non-visible Damage Detection:
– Utilized Computer Vision (CV) to detect visible and non-visible damages in accident-damaged vehicles, improving accuracy and comprehensive analysis.
– Developed an AI-powered model using high-detail 3D models to generate synthetic datasets, simplifying training processes.
– Developed a video-based measurement extrapolation system to estimate vehicle damage dimensions, enabling efficient decision-making.
Fraud Detection in Repair Costs:
– Implemented a fraud detection system to identify irregularities in repair cost claims, reducing financial losses.

Manager of the Machine Learning Department, handling a team of 15+ talented engineers and Data Scientist. Product Strategy and Development ownership. Keeping improving the future. Among other things, I was in charge of:

– Managed and coordination of 15+ Data Scientists and Python Engineers dedicated to applying AI for document processing.
– Consolidated and migrated from a segregated structure to a departmental approach, focusing on product development rather than customer projects.
– Developed and implemented KPIs and OKRs for the teams and department, ensuring alignment with company goals.
– Led the adoption of a Data Lake and transition from Data Warehouse, resulting in cost reduction and improved training efficiency.
– Defined an AI-based product roadmap with a focus on Natural Language Processing (NLP) and Computer Vision (CV) applications.
– Implemented an automated re-training system based on customer corrections, leading to an overall accuracy improvement of 5-10% across customers.

*Projects and AI Applications*
Transition to a Departmental Approach:
– Overcome the challenges of a segregated structure by transitioning to a unified departmental approach.
– Improved scalability and efficiency in incorporating new clients.
– Streamlined product development, resulting in cost savings and standardized maintenance processes.
Automated Re-training System:
– Developed an automated re-training system based on customer corrections.
– By incorporating customer feedback into the training process, accuracy improved by 5-10% across all customers.
Department Expansion in Europe:
– Conducted suitability analysis to determine target countries for expansion.
– Evaluated competition and studied hiring regulations and costs in each country.
– Gather necessary documentation and facilitated the establishment of the company in the selected countries.
– Successfully recruited suitable profiles in the target country to support the expansion plan.

Driving revenue growth, navigating COVID-19 challenges, securing partnerships, and making informed decisions through data-driven strategies, advanced predictive models, and coordinated teams.

Market Analysis and Revenue Growth:
– Developed and coordinated advanced predictive models to forecast market trends.
– Achieved a 20% revenue growth in the first year through accurate market predictions.
– Actively participated as a board member and company strategy coordinator.

COVID-19 Strategy and Market Expansion:
– Successfully changed the company’s path during the pandemic to avoid losses.
– Modified models to optimize inventory management for clients during the pandemic.
– Implemented adaptive service strategies to address emerging needs and challenges.
– Minimized losses and explored new markets.

Business Development and Partnerships:
– Led negotiations with different accounts and achieved significant milestones.
– Secured partnerships with three mid-size airlines.

Business Intelligence and Strategic Decision-Making:
– Utilized data-driven insights to formulate yearly strategies and fix KPIs.
– Conducted realistic analyses of company capabilities and resources.
– Studied customer and product requirements and international market trends.
– Identified target customers and evaluated their potential impact.

Multidisciplinary Team Coordination and Management:
– Oversaw the creation and coordination of a diverse team of 12 engineers.
– Managed specialists in data engineering and software engineering.
– Assessed the strengths and weaknesses of the engineering department.
– Determined staffing needs and defined suitable profiles.
– Implemented a streamlined and efficient selection process.
– Leveraged platforms like LinkedIn for active recruitment.
– Ensured alignment with company culture and role requirements.
– Considered hiring regulations and cost analysis for expansion in different countries.

Magnetic MRO is a leading aviation maintenance and repair organization. In my role as a Senior Powerplant Engineer and Data Science Engineer, I played a key role in implementing data science technologies and driving efficiency in the company’s operations.

*Key Achievements:*
– Data Science Technologies Implementation: Successfully implemented data-driven models and systems, such as the Stock Optimization Model and Repair Cost Predictor & Fuel Provisioning models.
– Stock Optimization Model: Optimized inventory storage, resulting in a 6% reduction in storage costs and a 35% increase in stock rotation.
– Fuel Predictor Model: Developed an annual fuel predictor model for airlines, enabling efficient fuel provisioning and cost optimization.
– Datalake Creation and Implementation: Established
the company’s first database using the Data Lake architecture, facilitating data utilization for various departments.

*Projects and AI Applications*
1. Stock Optimization Model:
– Implemented a model to improve stock rotation by considering future customer repair needs, minimizing excess purchases, and maximizing storage space utilization.
2. Repair Cost Predictor & Fuel Provisioning Models:
– Developed predictive models to estimate repair costs and optimize fuel provisioning for airlines, enhancing cost efficiency and customer satisfaction.
3. Datalake Creation and Implementation:
– Created the company’s first comprehensive database using the Data Lake architecture, enabling efficient storage, retrieval, and utilization of data across departments.
– Improved training and utilization capabilities of data for data scientists and analysts.Magnetic MRO is a leading aviation maintenance and repair organization. In my role as a Senior Powerplant Engineer and Data Science Engineer, I played a key role in implementing data science technologies and driving efficiency in the company’s operations. *Key Achievements:* – Data Science Technologies Implementation: Successfully implemented data-driven models and systems, such as the Stock Optimization Model and Repair Cost Predictor & Fuel Provisioning models. – Stock Optimization Model: Optimized inventory storage, resulting in a 6% reduction in storage costs and a 35% increase in stock rotation. – Fuel Predictor Model: Developed an annual fuel predictor model for airlines, enabling efficient fuel provisioning and cost optimization. – Datalake Creation and Implementation: Established the company’s first database using the Data Lake architecture, facilitating data utilization for various departments. *Projects and AI Applications* 1. Stock Optimization Model: – Implemented a model to improve stock rotation by considering future customer repair needs, minimizing excess purchases, and maximizing storage space utilization. 2. Repair Cost Predictor & Fuel Provisioning Models: – Developed predictive models to estimate repair costs and optimize fuel provisioning for airlines, enhancing cost efficiency and customer satisfaction. 3. Datalake Creation and Implementation: – Created the company’s first comprehensive database using the Data Lake architecture, enabling efficient storage, retrieval, and utilization of data across departments. – Improved training and utilization capabilities of data for data scientists and analysts.

Iberia MRO is a leading provider of aircraft maintenance, repair, and overhaul services. With a strong focus on engine maintenance, I worked as a Powerplant Engineer and Data Science Engineer, contributing to the optimization of internal processes and the efficient management of parts.

*Key Achievements*
Stock Optimization Model: Implemented a model to improve stock rotation and minimize excess purchases by considering future customer repair needs. By accurately predicting required parts based on expected repairs, the model achieved a 60% increase in stock rotation and saved 20% on storage costs.

Internal Logistic Route Calculator Model: Developed an optimizer for internal logistics based on daily maintenance orders and hotspots. The model generated a map of “hot zones” in the workshop by hour, enabling timely allocation of logistics personnel to those areas. This approach minimized waiting times and streamlined production.

*Projects and AI Applications*
– Efficient Inventory Management: Reduced the number of unused parts in the warehouse, freeing up valuable storage space.
– Cost Savings: Achieved significant cost savings by optimizing stock levels and minimizing excess purchases.
– Streamlined Procurement: Improved the procurement process for parts, ensuring timely availability and reducing delays in maintenance operations.
– Enhanced Logistics Efficiency: Improved the efficiency and punctuality of parts delivery through optimized internal routing based on maintenance orders.
– Enhanced Maintenance Processes: Implemented data-driven models and strategies to improve internal logistics and optimize parts management.
– Efficient Parts Predictor: Developed a predictive model to anticipate parts requirements based on historical data and maintenance schedules, enabling proactive planning and reducing downtime.
– Parts Stock Efficiency: Implemented the Stock Optimization Model, resulting in significant cost savings and increased stock rotation.Iberia MRO is a leading provider of aircraft maintenance, repair, and overhaul services. With a strong focus on engine maintenance, I worked as a Powerplant Engineer and Data Science Engineer, contributing to the optimization of internal processes and the efficient management of parts. *Key Achievements* Stock Optimization Model: Implemented a model to improve stock rotation and minimize excess purchases by considering future customer repair needs. By accurately predicting required parts based on expected repairs, the model achieved a 60% increase in stock rotation and saved 20% on storage costs. Internal Logistic Route Calculator Model: Developed an optimizer for internal logistics based on daily maintenance orders and hotspots. The model generated a map of “hot zones” in the workshop by hour, enabling timely allocation of logistics personnel to those areas. This approach minimized waiting times and streamlined production. *Projects and AI Applications* – Efficient Inventory Management: Reduced the number of unused parts in the warehouse, freeing up valuable storage space. – Cost Savings: Achieved significant cost savings by optimizing stock levels and minimizing excess purchases. – Streamlined Procurement: Improved the procurement process for parts, ensuring timely availability and reducing delays in maintenance operations. – Enhanced Logistics Efficiency: Improved the efficiency and punctuality of parts delivery through optimized internal routing based on maintenance orders. – Enhanced Maintenance Processes: Implemented data-driven models and strategies to improve internal logistics and optimize parts management. – Efficient Parts Predictor: Developed a predictive model to anticipate parts requirements based on historical data and maintenance schedules, enabling proactive planning and reducing downtime. – Parts Stock Efficiency: Implemented the Stock Optimization Model, resulting in significant cost savings and increased stock rotation.