On our journey, we have discovered that building AI that delivers value in the real world, requires much more than just an algorithm - it requires mastering the data layers the algorithms are trained and evaluated on. AI organization, the data development achievements of our group are at the heart of improvements in our AI accuracy and scalability. We are among the few companies paving the way for methodologies and architectural patterns (that don't exist in this field yet) as we go.
Our AI Data Development Group is the data analytics core in our AI algorithms development process, and has several main areas of responsibility:
Mining the optimal data for our datasets, based on a deep understanding of the nuances of medical data, medical workflows, and algorithms.
Evaluating our algorithms performances, in order to maximize their success in real-world environments.
Facilitating data-driven product decisions through in-depth analysis.
Technical project management of the dataset development process.
Responsibilities
End-to-end ownership and technical project management of dataset development projects, for training and evaluation of AI algorithms, from ideation to delivery. This responsibility includes:
The facilitation of the data across various teams.
Detecting errors and anomalies in the data, defining the most efficient and accurate tables and views per project, and researching these issues to verify the quality signature of the dataset, which will have a sound effect on the product outcome.
Developing the optimal data pipeline per project.
Facilitating data-driven product decisions, by deeply understanding the product questions and identifying whether there are cost-effective data analyses that could provide a valuable answer to this question, based on a deep understanding of the relevant clinical workflows and algorithms.
Mapping bottlenecks in existing processes, and leading development of tools, infrastructure, and mechanisms to enhance our efficiency and decision-making processes at all levels.
Continuously assessing the projects risks and successfully mitigating them.
Develop and execute control and review processes for improving the teams quality signature on deliverables.
Requirements: B.Sc in Engineering or Exact Science (M.Sc. or Ph.D - A major advantage).
Industry experience in hands-on data analysis/science :
Extensive hands-on experience with Python (pandas, numpy, matplotlib) and SQL.
Experience in analytics engineering - Developing data streams using DBT, Airflow and similar tools.
Ability to summarize and visualize complex data and insights.
Experience with presenting data insights to management, for healthy data-driven decision-making processes.
Hands-on experience in developing data analysis tools and methodologies in a R&D/production environment.
An amazing data analyst or scientist, with a proven record of excellence.
Experience with the realm of machine learning algorithms and AI development - A major advantage.
Great communication skills, and ability to create and maintain positive relationships with a diverse set of stakeholders
Problem-solving approach - a team player with the ability to work with stakeholders, translating their needs into requirements, and implementing the right solution.
Experience in project management and/or proven leadership skills.
Strong passion for the medical field.
This position is open to all candidates.