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Lead Machine Learning Engineer

Israel, Hod HaSharon

About The Position

ThetaRay is the leading provider of AI-based Big Data analytics. 

We are dedicated to helping financial organizations combat financial cybercrimes such as money laundering and fraud facilitating malicious crimes such as terrorist financing, narco trafficking, and human trafficking which negatively impact the global economy. 

Our Intuitive AI solutions for Anti Money Laundering and Fraud Detection enable clients to manage risk, detect money laundering schemes, uncover fraud, expose bad loans, uncover operational issues, and reveal valuable new growth opportunities.

We are looking for a Lead Machine Learning Engineer that will join our development team.

  • Develop ML focused solutions for the financial sector using best-of-breed tools combining ThetaRay unique algorithms with frameworks such as spark, MLflow, Airflow, etc. in cloud native environments.
  • Participate in research and technology innovation to develop more efficient and automated ways to handle ML/DL workloads.
  • Participate in new product/feature definitions and implementation
  • Work mostly with Python/Spark on Kubernetes environments.





  • 3+ years of experience as a data scientist/data engineer/big-data developer.
  • Experience in defining and implementing feature engineering processes for ML based products       
  • Experience in writing code in Python (and Pyspark) and using Python data-science frameworks.
  • BSc/BA in Mathematics, Physics, Computer Science, Economics or another related field.
  • Experience with building solutions with Big Data tools and frameworks such as Spark, Hadoop, etc.
  • Experience in developing machine learning oriented solutions for the financial sector - advantage
  • experienced with deep/machine learning framework such as TensorFlow/Keras/Sklearn - advantage
  • Experience in NLP - an advantage.
  • Great communication skills.
  • Ability to quickly learn new technologies, frameworks, and algorithms.
  • Very good English, written and verbal.

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