What will you do:
- Build, Lead and manage a team of Data Scientists and Analysts that will work closely with all the departments in the company (product, engineering, etc.).
- Develop and lead large-scale technical implementation of advanced Machine Learning algorithms for our web security products. Use innovative technologies that combine work with practical projects and research projects - from the data analysis stage to prototyping of new ideas, their implementation in a production environment, and then monitoring and maintaining them.
- Work on projects in the fields of supervised and semi-supervised learning, anomaly detection, time series forecasting, and more.
- Make an impact - Shaping the business, data science, and research directions by developing new algorithms and features.
- Assess the effectiveness and accuracy of significant data sources and data gathering techniques.
What you'll need:
- 3+ years of proven record leading a team of experienced and junior data scientists with clear KPIs and results (a multidisciplinary team is preferable).
- An M.Sc. in a quantitative field (e.g., Statistics, Economics, Sciences, Engineering, Computer Science).
- Proven hands-on experience developing and researching algorithms in data science/machine learning techniques and statistical methods, such as: boosting and bagging algorithms, time series models, clustering and anomaly detection methods, and more.
- Proven experience in taking projects through all the stages of a new ML model, from data analysis and labeling to prototyping, implementation in a production environment, and monitoring and maintenance. Delivery oriented.
- Proficiency with Python and SQL.
- Experience working with and creating data architectures.
- Independent and innovative with the ability to lead problem definition and goal definition processes.
- Fluent in English (speaking and writing).
Nice to have:
- Experience in MLOps methods – Docker, Kubernetes, Google MLOps, etc.
- Background in cyber-security or fraud fields.
- Experience with continuous learning methods.