Description:
If you are confident that you can ramp up quickly in some of the areas, we would like to get to know you and look forward to receiving your application!
- Contribute to data architecture, data modelling and data lifecycle management across the drug discovery and development domains covering multiple modalities including small molecule, biologics, antibodies, proteins & peptides, cell therapies, gene therapies, nucleotides, vaccines, and omics data.
- Design, Implementation, and management of enterprise-wide data models (conceptual, logical, and physical), data standards, master data, metadata, dictionaries, taxonomies, and ontologies.
- Apply FAIR data principles to modern data architectures.
- Improve data models, harmonisation, standardisation, contextualisation, and annotation for cross-domain data integration.
- Consulting to develop strategy, roadmaps, designs, guidelines for data selection, sourcing, synthesis, exploration, cleaning, curation, wrangling, integration, and quality control.
- Using your experience with data mesh, relational databases, data lakes, warehouses, big data platforms and object storage to deliver customer solutions.
- Nurture long term customer relationships with stakeholders, internal teams, vendors, partners and third parties to achieve alignment & consensus.
- Research, assessment & recommendations for technology, standards, and best practices for data governance and data management implementation.
- Estimate, plan, and deliver data strategy and data management projects.
Essential Skills:
Candidates without a higher degree are encouraged to apply if they possess a strong background in the practical application of Bioinformatics and Machine Learning within the biotech/pharma industry.
- Hold a higher-level degree or possess equivalent experience in a related science e.g. Computational Biology, Genetics, Bioinformatics, Cancer Biology Cell Biology, Microbiology, Pharmacology, Biochemistry, or Biomedical Science.
- Experience designing and deploying enterprise data architectures for these data domains in large Biopharma.
- Knowledge of industry standards and best practices e.g. FAIR
- Good understanding of concepts such as Modelling, Integrity, ETL, UML, OWL, RDF, Knowledge Graphs, Triple Stores, Taxonomy & Ontology.
- Experience with databases, data lakes, warehouses, big data platforms and object storage. e.g. Snowflake, Denodo, Databricks, Talend, Oracle, Kafka, Elastic, Hadoop, Cassandra, MongoDB, Redis, AWS, Azure, GCP.
- Experience of Data Analytics to surface actionable insights, and are familiar with modern data analytics tooling, methodology, and the Data Analytics lifecycle.
- Strong collaborations skills with the ability to manage several tasks simultaneously and a self-starting, proactive approach.
- Basic understanding of developing product roadmaps (ideally with data products) and articulating that roadmap to many different types of stakeholders.
- A skilled communicator able to convey your vision, roadmap, and progress.
- Proven experience of developing, implementing, and managing a Data Governance and Data Quality Management Framework and associated processes.
- Knowledge of regulatory frameworks such as GxP, GDPR and other privacy regulations to drive data governance ethics.
It is nice to have:
- Experience designing and building Scientific Informatics solutions in Life Sciences.
- Experience with technology change management in large organizations.
- Knowledge of statistics and data analytics & data visualisation tools.
- Experience with development and scripting languages (e.g. python, R, perl …)
- Experience with big data streaming technologies.