Data Scientist

1 semana atrás

Lisboa, Lisboa, Portugal Kaizen Gaming Tempo inteiro

We are Kaizen Gaming


Kaizen Gaming is the leading GameTech company in Greece and one of the fastest-growing in the world, operating in 13 markets with 2 brands, Betano & Stoiximan.


We always aim to leverage cutting-edge technology, providing the best experience to our millions of customers who trust us for their entertainment.

We are a diverse team of more than 2.200 Kaizeners, from 40+ nationalities spreading across 3 continents.

Our #oneteam is proud to be among the Best Workplaces in Europe and certified Great Place to Work across our offices.

Here, there'll be no average day for you. Ready to press play on potential?

Let's start with the role


Our teams comprise of three different roles, data scientists, machine learning engineer and data engineers so that they include the full skillset to deliver projects to production.

We are currently looking for data scientists in all levels to join our teams. The role should combine strong knowledge of machine learning algorithms, analytical skills, experimental design etc. to support the model development process.

As a Data Scientist you will

  • Translate business requirements to machine learning problems;
  • Perform EDA and feature engineering;
  • Run comparative experiments for model training;
  • Implement best practices on model selection, parameter tuning etc.

What you'll bring

Must have:

  • A background in Computer Science, Statistics, Math or related field;
  • Strong knowledge of machine learning algorithm and respective theory;
  • 28 years of handson experience in delivering machine learning models to production;
  • Knowledge of the Python machine learning ecosystem;
  • Experience with Spark (PySpark);
  • Solid software background in OOP;
  • Strong skills in teamwork, communication, and analytical thinking;
  • Fluency in English, both oral and written.

Nice to have:

  • Experience with Azure / Databricks;
  • Knowledge of Deep learning;
  • Experience in recommendation systems.
Artificial Intelligence (AI) #Machine Learning (ML) #Data Science (DS)

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