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Περιοχή

Εταιρία:
Channel VAS
Ημερομηνία Δημοσίευσης:
13-05-21
Τύπος απασχόλησης:
Πλήρης
Κωδικός θέσης εργασίας:
Κατηγορία εργασίας:
Πληροφορική
Περιοχή εργασίας:
Δήμος Αμαρουσίου

Machine Learning Engineer, Fintech

Description

Channel VAS, the global Fintech leader, is looking for you! A creative individual that wants to be on board our journey to redefine the Fintech world as we know it. Products and services delivered to over 500 million consumers in over 40 telco and finance partners in more than 30 countries.

If being a part of the world’s #1 company in mobile Fintech sounds cool to you, if you find the fields of Big Data, Analytics, Technology and Finance fascinating, then join our team of 200 likeminded individuals, that assist in evolving our technology, products, and services every day.

With unceasing growth in mind, we meticulously look for talented professionals in various positions. Professionals that will provide excellent and steer accessible financial services to most people possible at any chance.

We welcome you to share our vision to lead the global economic and social development, with financial inclusion for all, through mobile value-added services. Join us as we adapt to our new reality, during and after the pandemic and as we keep performing and offering the same level of extraordinary Fintech solutions and turning challenges into opportunities.

Machine Learning Engineersare significant contributors of Channel VAS’ data driven automated decision making and risk management. They have extensive experience with machine learning flows, and the development and deployment of advanced algorithms. They have the capability to (i) build ML flows , (ii) design and develop statistical and machine learning algorithms, and (iii) operationalize them in credit risk management. Machine Learning Engineers are part of a large team of 20 people, working closely with Big Data Engineers and Analysts.

What you will do

  • Designing, developing and maintaining of scalable, reliable and automated ML modeling pipelines, including big data processing, model training and evaluation, continuous integration and development, model quality monitoring and analytics
  • Utilizing programming and operations talents to apply the latest patterns in continuous delivery, cloud operations, containerization, and server-less technology and build the next generation of AI/ML platform
  • Dealing with Big Data (datasets in TBs with hundreds of columns), stored in Hadoop clusters
  • Solution implementation using ML libraries (e.g Scikit-learn, Keras, Tensorflow, Apache MLlib) and big-data applications and tools (Apache Spark)
  • Designing and implementing credit risk models and algorithms beyond the state of the art
  • Experiment with new features and representations that will enhance the performance of the deployed solutions
  • Continuously improving algorithms and methods through rigorous investigation and evaluation of alternatives

What you will bring

  • BSc and MSc in Data Science, Business Analytics, Statistics, Mathematics, or Computer Science from an accredited institution
  • 3+ years of industry experience with Applied Machine Learning, ML flows, Statistical Analysis and/or Data Science
  • Experience with software container technology (e.g., Kubernetes, Docker, cloud services/APIs)
  • Working experience with cloud computing platforms like Google Cloud Platform
  • Proven ability to build scalable software operating on large datasets and with a high degree of task parallelism in PySpark
  • A solid foundation in the core concepts of machine learning and statistics
  • Ability to efficiently search and understand the scientific literature of mathematical models, machine learning and AI
  • Ability to judge the relevance of existing models and algorithms to specific business needs
  • Passion for learning, exploring and developing new models and machine learning and AI algorithms
  • Ability to hit tight deadlines and work under pressure and strict attention to detail
  • Cutting-edge technologies aware, with the ability to learn and adapt to new technologies

Optional (will be considered a plus):

  • PhD in Computer Science, Mathematical Sciences or Finance from an accredited institution

Experience with distributed computing platforms such as Hadoop and Spark will be an asset

  • Practical experience in building data pipelines with big-data applications and tools (Apache Spark, Docker, Hadoop)
  • Experience building Docker containers and maintaining Kubernetes infrastructure
  • Communication skills
  • Creative skills

Benefits

  • Collaborative culture
  • Challenging work environment
  • Comprehensive private healthcare insurance
  • Company phone and laptop

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