Our solutions are used in more than 1000 hospitals, including leading health systems in the US. Funded by some of the industrys leading VCs,has raised more than $250 million to date, and was chosen as one of TIMEs 50 most genius companies.
About this role
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. At 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
Developing the optimal data pipelines per use case.
Defining the most efficient and accurate tables and views per project, detecting errors and anomalies in the data,, and researching these issues to verify the quality signature of the dataset, which will have a sound effect on the product outcome.
Technical support to 20+ analysts using DBT as their go-to tool for ELT.
Mapping bottlenecks in existing processes, and leading development of tools, infrastructure, and mechanisms to enhance our efficiency and decision-making processes at all levels.
Develop and execute control and review processes for improving the teams quality signature on deliverables.
B.Sc in Engineering or Exact Science (M.Sc. or Ph.D – A major advantage).
Industry experience in hands-on Analytics Engineering.
Extensive hands-on experience with Python (pandas, numpy, matplotlib) and SQL.
3+ years of developing data streams using DBT – MUST
3+ years using Airflow and similar tools
Ability to summarize and visualize complex data and insights.
Hands-on experience in developing data analysis tools and methodologies in a R&D/production environment.
Great communication skills, and ability to create and maintain positive relationships with a diverse set of stakeholders.
Proven experience in mentoring and developing team members, fostering a collaborative and growth-oriented environment.
Problem-solving approach – a team player with the ability to work with stakeholders, translating their needs into requirements, and implementing the right
