We are the 100% digital bank of the Santander Group and we are currently undergoing a technological transformation and international expansion.
In 2016 the re-launch of the Bank began and since then we have been in continuous expansion and growth, especially in our technological side. We work in a start-up format, using agile methodologies to take our clients' experience to the next level. In 2019 we launched the Bank in the Netherlands, Germany and Portugal and the next country will be Argentina, with others to follow.
Our culture makes us different; social and diversity clubs are part of our essence and allow us to live our culture every day.
We are a flexible and fast adapting team that currently telework most of the time using all kinds of communication tools, we have not noticed the change!
This role represents a critical bridge between data scientists and the rest of the business areas within the company. Ideally you have been a developer for a while, and now you want to transition towards the Data Science world. You don't feel confident enough about algorithm but you know how to overcome that lack of knowledge with your coding skills and quick prototyping attitude. You want to learn about the algorithmic secrets of Data Scientists and you know how to teach about software development best practices. You love open source projects and have contributed to some of them. You find yourself perfectly represented here: https://s3.amazonaws.com/assets.datacamp.com/blog_assets/Python_Web_Development_Data_Science_Full.png
In this job you will be learning and developing stuff all along the DataScientist and DataEngineering path, from the SQL basics to the deployment of complex algorithm based microservices.
The MLOps provides expert guidance and delivers through self and others to:
- Develop data-related product-prototypes, creating real-life integrations of AI-based models developed by data scientists.
- You get things done. You collaboratively create thoughtful plans, communicate them to others, hustle to get resources, and then execute. You are organized. You strongly favor simple and elegant solutions over complex ones.
- Simplified model deployment. Data scientists use a variety of modeling languages, frameworks, and tools. With MLOps, IT operations teams can quickly deploy models from a variety of languages and frameworks in production environments.
- Production life cycle management. The initial model deployment is the beginning of a long life cycle of updates to keep a machine learning model running. MLOps provides a means to test and update models in production without interrupting service to business applications.
- Impose software development methodologies and good coding practices for data-scientists.
- Organize and structure data science code to optimize the engineering of data usage.
- Automatizes everything that falls in your hands.
- Eases the access to documentation, insights and knowledge using the most accesible tools, and developing an open source culture.
Experience and knowledge:
- Degree: Computer Science / Telecom. / Maths / Physics / Industrial engineering
- Min. 3 years of experience working with big data (spark is a must, hadoop, hive, kafka).
- Experience building fast POCs and extracting conclusions
- Experience designing new aggregating tables from a DataLake
- Experience with different database structures, including SQL (postgres, mysql) and NOSQL (cassandra, redis, elastic search).
- Experience and expertise across data integration and data management with high data volumes.
- Experience with microservices.
- Experience with AWS ecosystem (AWS, EMR, s3, redshift, lambda, glue, athena)
- Experience working in agile continuous integration/DevOps paradigm and tool set (git, jenkins, sonar, nexus, jira, splunk)
- Experience with some SQL database structures. Ideally also NoSQL.
- Curiosity about predictive modeling, machine learning and data visualization, but no experience is required.
- Fluent English
Muestra tus habilidades a la empresa, rellenar el formulario y deja un toque personal en la carta, ayudará el reclutador en la elección del candidato.
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