Director of Data Science and AI

Location
Spain
Rate, USD
Not specified
Work schedule
Full Time,
Language skills
English, Spanish
Available for Hire
Yes
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About me

As the Senior Manager of Data Science & AI at Solera, I lead a dynamic team of over 10 engineers, dedicated to harnessing the power of Artificial Intelligence in transforming the core products of our organization.
With a career spanning more than a decade, I’ve had the privilege of immersing myself in the ever-evolving world of data science and machine learning, consistently pushing boundaries and achieving remarkable results across a diverse range of industries, including automotive, insurance, aviation, and technology.

My mission is simple yet impactful: to leverage data-driven insights as a strategic driver for optimizing operations, enhancing decision-making processes, and propelling business growth.
My forte lies in the development and deployment of advanced predictive models, the implementation of automation solutions, and the tenacity to solve intricate problems. I’ve also cultivated a holistic, business-oriented vision, demonstrated by my track record of aligning the goals of data science with the broader objectives of organizations, diligently managing performance metrics, and consistently delivering impactful results. Beyond my professional achievements, my passion is firmly rooted in innovation, a relentless pursuit of knowledge, and a genuine commitment to nurturing the growth of teams and organizations through strategic thinking.

Moreover, throughout my career, I’ve not only created and nurtured international teams of up to 20 individuals from different backgrounds and expertise levels, but also developed a unique hybrid perspective that seamlessly integrates business acumen, technological prowess, and a deep understanding of product dynamics.
My journey has been enriched by my adept negotiation skills, which have allowed me to engage with clients effectively and represent my organizations with distinction. Furthermore, my innate ability to discern the intricate needs of businesses, colleagues, and clients has been a cornerstone of my success. This skill empowers me to craft solutions that are not only efficient but also tailor-made to meet the unique challenges and aspirations of those I work with.


Professional area



Education

2017-10 / 2019-05 8/10 @ EAE Business School

Executive MBA

2011-09 / 2013-06 3.2 GPA @ University Carlos III

Masters on Robotics and AI

2006-09 / 2011-06 3.2 GPA @ University Carlos III

Industrial Engineering


Experience

March 2022 Senior Manager of Data Science @ Solera Inc.

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.

April 2021 - March 2022 Manager of Machine Learning @ Infrrd

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.

October 2018 - March 2021 Head of Engineering & Data Science @ AOG-247

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.

January 2016 - February 2019 Senior Powerplant Engineer & Data Science Engineer @ Magnetic MRO

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.

January 2013 - January 2016 Powerplant Engineer & Data Science Engineer @ IBERIA MRO

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.


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