Head of Data (Strategy for Acquisition & Generation), London

Head of Data, London

Isomorphic Labs is a new Alphabet company that is reimagining drug discovery through a computational- and AI-first approach.

We are on a mission to accelerate the speed, increase the efficacy and lower the cost of drug discovery. You'll be working at the cutting edge of the new era of 'digital biology' to deliver a transformative social impact for the benefit of millions of people. 

Come and be part of a multi-disciplinary team driving groundbreaking innovation and play a meaningful role in contributing towards us achieving our ambitious goals, while being a part of an inspiring, collaborative and entrepreneurial culture.

Your impact 

As the Head of Data reporting to the CTO, you will be responsible for the company’s data assets and data strategy, including data procurement and generation, as well as data lifecycle and data management processes to ensure that Isomorphic Labs has access to and rigorously protects business-critical datasets and can generate the most value from this data. As part of this role you will build the world’s most comprehensive and high quality life science dataset for machine learning for drug discovery. You will collaborate closely with other senior leaders in the organisation, including heads of Data Engineering, Machine Learning Research, Product, Computational Biology, Drug Discovery, Business Development, Information Security, Legal and others.

Your responsibilities

• Develop, implement and monitor a comprehensive data strategy that aligns with the company's goals and positions data as a strategic asset and business tool.
• Procure data relevant for machine learning model development and other scientific analysis purposes, including public and commercial datasets, experimental data generation, and strategic data partnerships with other industry players and academic consortia.
• Liaise with a wide network of external data providers to gather data.
• Partner with other team members to define the data lifecycle and data management standard methodologies.
• Partner with ML Research and Engineering to advise data management and analysis infrastructure development.
• Define internal data structures, representations and ontologies across a wide variety of life science and medical research datasets, including chemical structures, SAR, reactions, molecular dynamics, -omics, imaging, and clinical data.
• Lead data curation and data quality assurance processes and ensuring that the company’s data is adequately integrated and usable for machine learning purposes.

Your skills and qualifications 

Essential

• PhD in Chemistry, Biology, Statistics, Computer Science or equivalent professional experience
• Extensive understanding of life science experimental data generation techniques and common assays used in research and drug discovery including spectroscopy, spectrometry, crystallography, microscopy, binding, ADME, cytotoxicity, and phenotypic assays, NGS, immunohistochemistry, RNA-seq, cell-based assays.
• Experience with a variety of life science data types and ontologies including -omics, imaging, and clinical data, representations of biomolecules and chemical reactions, Gene Ontology, Disease Ontology, SNOMED, CHEMINF
• Experience working with external life science databases and biobanks such as NCBI, Cosmic, ClinVar, UK Biobank, TCGA, dbSNP, OMIM, gnomAD, GDC, PDB, ChEMBL. 
• Experience managing a large scale data coordination centre, data repository, or biobank.
• Experience performing comprehensive data curation and data quality assurance.
• Experience managing external data vendors, Contract Research Organisations, and working within scientific consortia.
• Strong internal and external stakeholder management skills with demonstrated ability to integrate and align a variety of complex opinions and work through influence to achieve ambitious scientific and organisational outcomes.
• Strategic planning and roadmap development. 
  
  

Nice to have

• Understanding of Machine Learning and software engineering principles and standard methodologies.
• Experience leading teams of data curators and data analysts.
• Understanding of statistical analysis, design of experiments, and data visualisation. 
• Detailed understanding of drug discovery, pharma, and medical research.