QuantHealth is an AI startup supporting drug development and clinical trials. Our ground-breaking clinical trial simulator can run thousands of trials in parallel to de-risk and optimize upcoming clinical trials. Our solution, based on a huge dataset of 350M patients and 100K drugs, drastically reduces drug development costs, shortens development timelines. We are venture backed, based in Israel, and have reputable pharma customers in both Europe and the US. This is a fantastic opportunity for a data scientist who wants to join a fast-growing startup and tackle complex challenges.

Job Summary:

As a Data Scientist in our growing team, you'll face fascinating challenges while working with vast clinical data covering molecules and patients. You'll utilize your data science skills to develop and implement ML models and pipelines, improving real-life clinical trials. Collaborating with software engineers, clinical data scientists, and researchers, you'll analyze large datasets using Python data stack, validate predictive models, and contribute to enhancing our clinical trial simulation platform. Join us on this exciting journey to make a significant impact in healthcare through data science innovation.

Responsibilities:

• Develop and continuously improve predictive models and ML/data pipelines using modules like PySpark, PyTorch, MLflow.
• Implement and manage robust MLOps pipelines to ensure the seamless deployment, monitoring, and maintenance of machine learning models in production.
• Design and operationalize DataOps strategies to enhance data quality, accessibility, and integration across distributed systems, utilizing technologies such as Apache Kafka and Apache Airflow.
• Utilize statistical techniques to evaluate models and optimize clinical study design.
• Collaborate with cross-functional teams, including clinical data scientists, software engineering, and product development, to design and implement advanced data analytics solutions for our platform.
• Stay current with the latest developments in data science, NLP, and clinical trials research, proactively sharing knowledge and best practices with the team.
• Participate in the development and maintenance of technical documentation, including specifications, user guides, and training materials.

Requirements:

• 3-4 years of industry experience in data science and machine learning.
• BSc in a relevant field, such as computer science, bioinformatics, or a related quantitative discipline.
• Strong understanding of statistical concepts and techniques, including regression analysis, hypothesis testing, and data visualization.
• Proficient in Python, with extensive experience using machine learning libraries (e.g., scikit-learn, TensorFlow, or PyTorch) and tools for data pipeline management and automation (e.g., Apache Airflow, Kubernetes, Docker).
• Excellent problem-solving abilities and a strong analytical mindset.
• Experience designing and managing MLOps and DataOps workflows, preferably in a clinical or pharmaceutical environment.
• A collaborative team player with strong interpersonal and communication skills.

Advantage:

• M.Sc in Computer Science, Data Science, or a related field with a focus on Software Engineering or Data Operations. Specialization in machine learning, data pipeline architecture, or cloud computing is highly desirable.
• Familiarity with cloud computing platforms (e.g., AWS, Azure, Google Cloud).
• Experience working with clinical data or knowledge of clinical trial operations and regulations.
• Proficiency with PySpark for big data processing, demonstrating the ability to build robust data pipelines that support machine learning workflows.
• Clinical/biomedical experience or knowledge from either education or industry.

Notes:

• We understand that no candidate may fulfill 100% of the qualifications. Trust your experience and apply! We value individuals who are passionate about learning and are eager to bridge any knowledge gaps through their work.

• We mix remote and in-office work. Most of the team is in the office two days a week and works from home the remaining days.