Data Scientist; Analytics

Requirements

• Strong background in statistics and data analysis
• Strong coding skills in Python and SQL including experience using data science toolkits such as Num Py, Sci Py, or Pandas
• Proven experience in a data science or platform analytics role ideally in a drug discovery company
• Experience defining and measuring metrics for engineering or product performance (e.g., efficiency, bottlenecks, KPIs)
• Familiarity with the early drug discovery process
• Ability to communicate complex data insights to a diverse range of stakeholders, including engineers and scientists
• (Desirable) Familiarity with data engineering concepts and experience with running jobs on Cloud-based infrastructure

• This is an exciting opportunity to join the data team at Iso Labs
• You will work alongside Data Curators, Engineers, AI experts, and Drug Discovery scientists to drive the analytics that underpin our drug design engine
• As a Data Scientist focused on platform analytics, you will be responsible for capturing and analysing data to understand how our technology and drug design processes are performing
• You will provide the robust data science expertise needed to turn business intelligence into actionable insights, helping Product, Data, Engineering, Research, and Drug Discovery teams make the best possible decisions to accelerate the delivery of new medicines
• Analyze business intelligence data to provide robust and insightful outputs that drive decision-making across product, engineering, and research teams
• Partner with the Engineering, Product, and drug discovery teams to implement telemetry and traceability, ensuring we capture drug design intent and metadata effectively
• Design and track key metrics (KPIs) to evaluate if we are building the most efficient drug design engine and progressing towards our long-term goals
• Conduct internal and external benchmarking on Machine Learning model performance and drug discovery processes
• Identify bottlenecks in the end-to-end delivery system to optimize real-world delivery
• Collaborate with engineering to build an analytics layer that supports generalizable drug design and helps determine the impact of our models on success rates