Data Scientist

A Fintech company has launched a platform providing advanced financial solutions for small and medium-sized businesses using cutting-edge AI and ML-based banking technology.

As part of the team focusing on Data Science tasks in the areas of Risk and Loans, the role involves research and development of machine learning models in Python using PyTorch, TensorFlow, Scikit-Learn. Tasks include working with tabular data, data collection, optimization, SQL, ideation, training, testing, deployment, monitoring, and scaling in an AWS environment.

Located in Tel Aviv, near the train station, the company adopts a hybrid work model with three days remote. With a global workforce of 600 employees, nearly 200 of them are based in Israel.

Responsibility-

• Apply advanced statistical and predictive modelling techniques to design, build, maintain, and improve multiple decision-making systems.
• Apply modern software development techniques to implement models in a scalable, robust, and maintainable way.
• Collaborate on highly technical MLOps projects - from redesigning working infrastructures, maintaining and improving the current models and underlying systems and integrating new technologies into the data science workflow.
• Manage initiatives to define and implement dedicated data collection procedures for building analytical systems.
• Work closely with your fellow Data Scientists, the R&D department, and cross-functionally with teams across the US and Israel.

Requirements-

• Master’s Degree or Ph.D. in Statistics, Computer Science, Engineering, or a related quantitative field.

• 4 years of working experience as a full-stack Data Scientist or equivalent.

• Experience developing and deploying scalable production-level code in Python.

• Working with ML frameworks such as PyTorch, TensorFlow, and Scikit-Learn.

• Experience working with Relational Databases and writing optimized SQL queries.

• Theoretical and Practical experience developing and deploying ML models (Ideation, Training/Testing, Deployment, and Monitoring).

• Ability to evaluate complex data sets and drive meaningful insights.

• Experience communicating technical concepts and findings to a non-technical audience.

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