Data Scientist (Analytics), London

Isomorphic Labs (IsoLabs) was launched in 2021 to advance human health by building on and beyond the Nobel-winning AlphaFold system. Since then, our interdisciplinary team of drug discovery experts and machine learning specialists has built powerful new predictive and generative AI models that accelerate scientific discovery at digital speed.

Our name comes from the belief that there is an underlying symmetry between biology and information science. By harnessing AI’s powerful capabilities, we can use it to model complex biological phenomena to help design novel molecules, anticipate how drugs will perform and develop innovative medicines to treat and cure some of the world’s most devastating diseases.

We have built a world-leading drug design engine comprising AI models that are capable of working across multiple therapeutic areas and drug modalities. We are continually innovating on model architecture and developing cutting-edge capabilities to advance rational drug design.

Every day, and with each new breakthrough, we’re getting closer to the promise of digital biology, and achieving our ambitious mission to one day solve all disease with the help of AI.

This is an exciting opportunity to join the data team at IsoLabs. 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.

• Strong background in statistics and data analysis.
• Strong coding skills in Python and SQL including experience using data science toolkits such as NumPy, SciPy, 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. 

• Familiarity with data engineering concepts and experience with running jobs on Cloud-based infrastructure.

We are guided by our shared values. It's not about finding people who think and act in the same way. These values help to guide our work and will continue to strengthen it. 

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