Work on data science projects such as segmentation, recommendation, and prediction. Assess data availability and data quality. Formulate insurance business, use cases in machine learning terms. Define tests and experiments that aim to assess the relevancy and feasibility of machine learning solutions to business use cases. Apply state-of-the-art machine learning techniques to insurance data such as model selection, training, testing and validation.
Experience & Qualifications:
- Sc Mathematics/ Economics/ Computer Science / Statistics or equivalent
- At least 3 years of experience as a data scientist
- You must be highly proficient in R/Python and relevant libraries.
- Knowledge of a variety of machine learning techniques (exploratory data analysis, predictive modeling, supervised/unsupervised machine learning, anomaly detection) and their real-world advantages/drawbacks – Must.
- Proficiency in using query languages, such as SQL, Spark DataFrame API, etc. – an advantage.
- Experience with Deep Learning, particularly when applied to insurance data – an advantage
- Excellent communication skills in both Hebrew and English.
- Background in insurance – advantage.
- Self-motivated and team player that has the drive to learn and master new technologies and techniques