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Luis Nicolas-Alonso, Machine Learning Developer in Barcelona, Spain
Luis Nicolas-Alonso

Machine Learning Developer in Barcelona, Spain

Member since June 21, 2018
Luis is a seasoned data scientist with a strong background in mathematics, software engineering, and machine learning. He has a proven track record of success developing scalable data analytics applications in the cloud and collaborating with technical and non-technical stakeholders. Luis is passionate about running data science projects with agile management, test-driven development, and continuous delivery.
Luis is now available for hire

Portfolio

  • Xapo
    Python, SQL, Nifi, Redshift, Tableau, Google Data Studio, Excel
  • Vodafone
    Hadoop, HDFS, Impala, Cloudera, PySpark, Python, Scala, Git, Tensorflow...
  • Jaguar Land Rover
    Hadoop, Spark, Scala, Python, R, Tableau, HBase, Cassandra, Kafka, BigQuery...

Experience

  • Machine Learning, 7 years
  • SQL, 5 years
  • Python, 5 years
  • Spark, 3 years
  • Computer Vision, 2 years
  • Natural Language Processing (NLP), 2 years
  • Scala, 1 year
  • Apache Kafka, 1 year
Barcelona, Spain

Availability

Part-time

Preferred Environment

Mac OS X, Git, JIRA

The most amazing...

...project I've developed was an intelligent car that offered a fully personalized driving experience using deep learning and natural language processing.

Employment

  • Data Engineer

    2019 - 2019
    Xapo
    • Built a data warehouse on AWS (Airflow, Glue, Lambda, Redshift) to generate operational dashboards at every level in the business (customer support, compliance, debit card, etc.).
    • Created ETL data pipelines with NiFi to sync data with databases in production.
    • Created datamarts in BigQuery easily accessible using Excel, Tableau, or Google Data Studio.
    • Collaborates with all areas of the organization to ensure data quality and integrity.
    • Ensured compliance with the organization’s data governance policies.
    Technologies: Python, SQL, Nifi, Redshift, Tableau, Google Data Studio, Excel
  • Data Scientist (Remote)

    2017 - 2018
    Vodafone
    • Designed and developed large-scale machine learning algorithms with Impala, Spark, R (Shiny) and Python (Pandas/Numpy/Plotly/TF/Keras) to improve customer retention and product recommendation, analyze customer social network, and optimize marketing campaigns.
    • Analyzed WhatsApp usage patterns with Spark to understand customer social network. This information would be used for marketing.
    • Analyzed network performance and net promoter score to improve mobile network based on customer satisfaction.
    Technologies: Hadoop, HDFS, Impala, Cloudera, PySpark, Python, Scala, Git, Tensorflow, Keras, Pandas, Numpy, Plotly
  • Data Scientist

    2015 - 2017
    Jaguar Land Rover
    • Managed stakeholders, planned projects, and designed a strategic roadmap for the Research DataLab team.
    • Directly involved in deploying a scalable automotive data logging system on a fleet of 150 engineering vehicles, and developing large-scale data pipelines on AWS.
    • Analyzed driving patterns to enhance advanced driver-assistance systems, anomaly detection to improve vehicle reliability and enable failure prediction, analysis of vehicle component usage to optimize reliability and cost.
    • Created a data quality testing framework to ensure data integrity.
    • Designed and developed a library that made it easy to run queries on vehicle data.
    Technologies: Hadoop, Spark, Scala, Python, R, Tableau, HBase, Cassandra, Kafka, BigQuery, ElasticSearch, Logstash, Docker
  • Data Scientist

    2015 - 2015
    Jaguar Land Rover
    • Contributed to the design and development of an intelligent car and native cloud application on AWS to offer fully personalized driving experience.
    • Designed performance metric to measure the quality of service for each component of the application.
    • Developed machine learning models to predict user driving routines. Predictions were used for car preconditioning, fuel consumption estimation, destination prediction, or estimating time of arrival.
    • Created a model to predict user destination based on calendar and email using natural language processing.
    Technologies: AWS, Cassandra, HBase, Python, Java, Scala, Kafka, Docker
  • Machine Learning Engineer

    2012 - 2015
    Biomedical Engineering Group
    • Improved state-of-art motor imagery brain-computer interface performance by 10% using online adaptive ensemble classification.
    Technologies: MATLAB, Hive
  • Research Scientist

    2014 - 2014
    Brain Computer Interface Group, University of Essex, UK
    • Worked on advanced brain signal processing with multitask learning, transfer learning, domain adaptation, deep learning, auto-encoders, and deep belief neural networks.
    Technologies: MATLAB, Python
  • Software Engineer

    2010 - 2012
    Agroguia
    • Developed a machine learning application that allows steering a tractor by means of an EMG-based human-machine Interface.
    Technologies: C++, Java, Digital Signal Processing, GPS

Experience

  • Go I-PACE App (Development)
    https://media.jaguar.com/news/2018/07/go-i-pace-app-puts-electric-jaguar-your-pocket

    One of my last projects at Jaguar Land Rover.

    Go I-PACE helps customers understand the potential cost savings of going electric compared to their existing vehicle. would-be buyers. The app estimates how I-PACE would fit into your life based on personal journey data.

    The Go I-PACE app captures journey data to calculate potential cost savings, show how much battery would be used per trip and tell users how many charges they would need in a week if they were driving the I-PACE.

    Calculates the range expected from a full charge based on your vehicle use, the number of charges required in a typical week and how frequently you would need to top up mid-journey.

    It can also distinguish between different modes of transport to make sure it collects accurate data, even prompting users to confirm that individual trips were made by car for unusual routes – for instance on journeys made by cycling rather than behind the wheel.

  • Self-learning Car (Development)
    https://www.youtube.com/watch?v=F923EuB06CI

    Responsible for the delivery of the data analytics components of a car and mobile application to offer fully personalized driving experience to Jaguar Land Rover customers and help prevent accidents by reducing driver distraction.

    Main responsibilities and goals involved:

    ● Define and implement scalable real-time workflow to load data, quality management, and distribution across various system using Big Data technologies on Amazon Web Services.
    ● Contribute to the software development lifecycle including the analysis, architecture, design, implementation, and QA.
    ● Hands-on work directly implementing complex machine learning solutions using Natural Language Processing, recommender systems, neural networks and/or deep learning.
    ● Write technical documentation and presentation of results to technical and non-technical stakeholders.

  • Sensors (Development)
    https://github.com/lnicalo/Sensors

    Scala package to process time series from different sensors with Spark.

    Processing time series collected from different sensors poses several challenges as a result of data may not be aligned or have the same time sampling. Writing data queries can be quite hard for data scientists because data cannot be expressed in a tabular form.

    This library makes it easy to write queries with this kind of datasets.

  • Driver Profile Analysis (Development)
    https://media.licdn.com/media-proxy/ext?w=800&h=800&f=n&hash=EH1S%2FD9kOaaPI%2B0pAzSgHLxoFyQ%3D&ora=1%2CaFBCTXdkRmpGL2lvQUFBPQ%2CxAVta9Er0Vinkhwfjw8177yE41y87UNCVordEGXyD3u0qYrdf3Pue8_WcbfyuQgXKikclAU6e_KhRmWwD8C6KI3sKIgggsThIY24ZxUBbFImi24

    Analysis of daily driving patterns of Jaguar Land Rover customers:

    - Fuel consumption
    - Daily in-car time
    - Commute schedule
    - Regular routes
    - Total distance
    - Journey duration
    - Driving style
    - Refuelling events
    - Phone call patterns
    - Heated and cooled seat usage
    - Phone call pattern
    - Radio stations

  • Data Science Competition - CONNECTOMICS - (16th / 143) (Other amazing things)
    https://www.kaggle.com/c/connectomics

    This challenge will stimulate research on network-structure learning from neurophysiological data, including causal discovery methods.

    The goal of the data science competition was to predict the directed connection between 1000 neurons based on their time series of the activity.

    My solution involved a mixture of several features such as correlation, mutual information, partial correlation, spectrogram, and frequency analysis.

  • Data Science Competition - Grasp-and-Lift EEG Detection - (15th / 379) (Other amazing things)
    https://www.kaggle.com/c/grasp-and-lift-eeg-detection

    This competition challenges you to identify when a hand is grasping, lifting, and replacing an object using EEG data that was taken from healthy subjects as they performed these activities. A better understanding of the relationship between EEG signals and hand movements is critical to developing a BCI device that would give patients with neurological disabilities the ability to move through the world with greater autonomy.

    My solution involved a deep neural net developed with Python using Theano and Lasagne.

Skills

  • Languages

    Python 3, SQL, Python, Python 2, R, Scala, Java, C++
  • Frameworks

    Spark, RStudio Shiny, Hadoop
  • Libraries/APIs

    Pandas, Spark ML, Keras, Sklearn, NumPy, Scikit-learn, TensorFlow, Spark Streaming, Google Cloud API
  • Tools

    Spark SQL, Tableau, Impala, BigQuery, PyCharm, Git, Superset, Logstash, AWS CloudWatch, AWS ElastiCache, Cloudera, Amazon SageMaker
  • Other

    Predictive Modeling, Big Data, Statistics, Recurrent Neural Networks, Artificial Neural Networks (ANN), Convolutional Neural Networks, Deep Neural Networks, Neural Networks, Deep Learning, Data Visualization, Computer Vision, Natural Language Processing (NLP), Agile Data Science, Internet of Things (IoT), Google BigQuery, Kappa Architecture, Lambda Functions, Machine Learning, Google Cloud ML
  • Platforms

    Spark Core, Docker, Apache Kafka
  • Storage

    Redshift, MySQL, Apache Hive, Elasticsearch, HBase, HDFS, Cassandra, Google Cloud
  • Paradigms

    Lambda Architecture, Microservices Architecture, Agile, Siamese Neural Networks

Education

  • Ph.D. in Biomedical Engineering
    2013 - 2019
    University of Valladolid - Valladolid, Spain
  • Master's degree in Information Technology (Data analysis)
    2011 - 2012
    University of Valladolid - Valladolid, Spain
  • Master of Science degree in Electronic and Telecommunication Engineering
    2005 - 2011
    University of Valladolid - Valladolid, Spain
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