Patricia Gomez Bello

Professional Summary

Expert product manager with nineteenth years of experience in public, private universities and in different private sectors such as “Banking, Telecommunications, Car Industry, Airplane Industry, Department Stores”. Committed to procuring and governing IT resources and support staff to deliver innovative solutions meeting or exceeding budgetary and performance expectations. Skilled in developing training and coaching programs for contact center agents and onsite technicians. Demonstrated history of improving customer satisfaction ratings and increasing first call resolution rates. Strong leadership skills balanced with collaborative focus to forge solid relationships with coworkers while giving clear direction to teams and departments. – Effective planning and resource management to accomplish both immediate and long-term goals. – Excellent problem-solving abilities permit me to devise unique, innovative solutions in SAAS Architectures, Digital products and Artificial Intelligent applications. – Extensive technical knowledge includes Unix, Linux, Windows, Mac OS, Spark, Python, Scala, Splunk, Ruby, Go, C, C++, Java, Android, Swift, Node.js, Javascript, Keras, Tensorflow, Tensorflow Serving, Elasticsearch, Kibana, Logstash, BigQuery, Big Data, Aurora, PostgreSQL, Amazon EC2 AWS, Amazon Alexa - Bot, K-Mean Clustering, Hashtables, Hashmaps, Docker, Devspaces, Kubernete, Jenkins, MongoDB, Web Services, Agile, Natural Language Processing, Natural Language Understanding, Machine Learning, Neural Networks and Deep Learning.

Adept at patient, detailed instruction by which I teach others and troubleshoot technical problems.

Machine Learning Architect & Manager

Repsol

Jun 2017 – April 2018 | Madrid, Spain

I was currently working in the Automation & Process Deployment core team to lead Accenture Ticket Resolver (ATR) initiative. It is based on Splunk Machine Learning and six machine learning models: assignment group, category, solution id, resolve time, ticket management, unique knowledge. I have been working on the installation and configuration of the architecture of Accenture Ticket Resolver and the getting high percentages of prediction to make suggestions to managers with respect to how to resolve the incidents as follow:

Assignment group - with this prediction helps managers to know what group has the expertise to resolve the ticket;

Category - this prediction helps to make sure a newly created ticket is categorized correctly;

Solution id - This prediction is a link (unique identifier) to define which pre-defined solution should be applied to the ticket. This id also known as the deep link is used for back-end configuration;

Resolve time - ATRs machine learning capability will predict the time a given ticket will take to resolve;

Ticket management - This prediction, aligned to solution_id defines the ticket management method used to present the correct solution for the ticket (eg. Work instructions, bespoke third-party tool etc);

Unique knowledge - This entry is aligned with the ticket management and solution_id outputs and is a unique identifier for the solution definition (work instruction) predicted for use in solving the ticket.

Moreover, I was collaborating with the Agile CoP in Madrid, acting as Agile Coach supporting delivery and transformation programs - Smart Accenture Platform.

Artificial Intelligence Iberia & ELK, Dockers, Kubernete

Software Engineering Manager

Banc Sabadell

Apr 2016 – May 2017 | Madrid, Spain

I was working hard with the AI technologies such as Amazon, IBM, Google, Microsoft, and open sources. Defining intelligent chatbots (Carla, Michelle, Amelia) that their answer is coming from algorithms of Machine Learning and Deep Learning using big data sets. Additionally, these chatbots are applying Natural Language Processing, Computer Vision and Predictions/ Insights/Recommendations. At the same time, I was creating Nahui" (an agent intelligent based on a virtual brain to analyze, to clean, to organize, and to fix code in accordance with the software quality standards). To create Nahui, I was using Machine Learning & Deep Learning systems such as TensorFlow.

Also, I was acting as Java architect to solve any necessity of legacy applications of Sabadell Bank in Madrid. I was working with DevOps Tools such as Kubernete, Docker and the Stack of Elasticsearch - ELK (Elasticsearch, Logstash, Kibana).

Additional capabilities: leadership, goal-oriented, team creation & management, offshore management experience, commitment.

Technology Consulting Manager

Liberbank

Jan 2016 – Mar 2016 | Madrid, Spain

I was part of Financial Services Technology Advisory. I was an advisory service on the important technology topics for our FS clients CxO agendas. Through this, I was helping to enable them to put together their strategies and take these through piloting and implementation all the way to operationalizing in the new. We sat at the intersection between our industry and the newest technology and are the catalysts for the transformation happening with technology in the FS industries.

I was in charge of technical and business requirements from analysis, design, develop, and configure software systems to meet market and/or client requirements either end-to-end from analysis, design, implementation, quality assurance (including testing), to delivery and

maintenance of the software product using the Agile Tools, Continuous Integration, Continuous Delivery, and DevOps methodology.

Digital Scholarships

Software Engineering Manager

May 2015 – Dec 2015 | Madrid, Spain

I managed the content of the new Java Course (JSE 1.8, JEE, Spring) for the beginners’ trainees: "Agile and Continuous Integration, Agile and Continuous Delivery, JavaScript, AJAX, AngularJS, SonarQube, Jenkins" for the Digital Technologies projects such as Big Data, Salesforce, Mobile - Android, IOs, eCommerce. I was part of the Training team. The main roles were

- Training coordinator of Accenture Digital in Spain.

- New joiners training

- Accenture Language Program

Since I was hired by Crossover, I was developing a role of Chief Architect of Data Scientist to industrialize products in accordance with Artificial Intelligence (Machine Learning and Deep Learning) using agile, docker, kubernetes, and Jenkins.

In one month, I have included “EU General Data Protection Regulation (GDPR) on the INFER product (www.infer.com). In fact, in his machine learning algorithms that are based on scoring and statistics models (prediction). These machine learning models were developed by python, scikit-learn and tensorflow executed by coffescript, sass, grunt, bower, npm, aurora, zookeeper, bladerunner and salesforce.

On June, I started with CHUTE product getchute.com that is based on Deep Learning models to classify and to recognize images by Convolutional Neural Networks. I am in charge to transfer learning from Caffe GoogleNet Inception V1 to tensorflow. Also, I am responsible to train and to recognize images through nvidia-cuda-gpu, when there is a new brand or object or scene on a new campaign for the marketing of any product. CHUTE is developed by npm, deep detect server, javascript, go, html, groovy, ruby, swift, C++, java, TypeScript, mongo db, redis, elasticsearch, sidekiq, bigquey and ec2 aws machines. Finally, I was part of the integration team to migrate CHUTE product from Circle CI to docker, kubernetes and Jenkins.

Intelligent Internet for Children