Staff NLP Data Engineer and Team Lead

4 weeks ago

London, Greater London, United Kingdom GlaxoSmithKline Full time
Job Summary

The Onyx Research Data Tech organization is GSK's Research data ecosystem which has the capability to bring together, analyze, and power the exploration of data at scale. As a Staff NLP Data Engineer and Team Lead, you will partner with scientists across GSK to define and understand their challenges and develop tailored solutions that meet their needs. The goal is to ensure scientists have the right data and insights when they need it to give them a better starting point for and accelerate medical discovery.

About the Role

Data Engineering is responsible for the design, delivery, support, and maintenance of industrialized automated end to end data services and pipelines. They apply standardized data models and mapping to ensure data is accessible for end users in end-to-end user tools through use of APIs. They define and embed best practices and ensure compliance with Quality Management practices and alignment to automated data governance.

This role is responsible for building and leading a scrum team of world-class NLP data engineers focused on building automated, scalable, and sustainable pipelines to account for evolving scientific needs. They support the head of Data Engineering in building a strong culture of accountability and ownership in their team, as well as instilling best-in-class engineering practices (e.g., testing, code reviews, DevOps-forward ways of working).

Key Responsibilities

• Lead a team of NLP data engineers in delivering data and knowledge products that advance GSK R&D
• Architect the data delivery and operational strategy for the NLP data engineering team; Deconstruct a complex and ambiguous data or knowledge request into a detailed strategy to make decision, anticipates future issues, and drive engineering efficiencies
• Partner with AIML and knowledge graph platform team to build, test, and deploy NLP pipelines, systems and solutions
• Apply graph-based data modelling techniques for efficient organization, integration, and data retrieval to ensure system flexibility and maintainability
• Design innovative strategies beyond the current enterprise way of working to create a better environment for the end users, and able to construct a coordinated, stepwise plan to bring others along with the change curve

Requirements

We are looking for professionals with these required skills to achieve our goals:
• Bachelor's degree in Data Engineering, Computer Science, Software Engineering or related field.
• 8+ years of Data Engineering experience
• Experience in Natural Language Processing algorithms and deep learning methods
• Experience with building end-to-end systems based on machine learning or deep learning methods
• Cloud experience (e.g., AWS, Google Cloud, Azure, Kubernetes)

Preferred Qualifications

We are also looking for professionals with the following characteristics:
• Demonstratable experience overcoming high volume, high compute challenges
• Familiarity with orchestrating tooling
• Experience in automated testing and design
• Experience with DevOps-forward ways of working
• Nice to have good understanding of ontologies and semantic harmonization of data across sources
• Deep knowledge and use of at least one common programming language: e.g., Python, Scala, Java
• Deep experience with common big data tools (e.g., Spark, Kafka, Storm, ...)
• Proven experience with machine learning algorithms and NLP frameworks like Pytorch, Tensorflow, Spacy, etc.
• Proven track record of working with knowledge graphs and graph databases, and in general good understanding of database concepts
• Proficiency in semantic web technologies (SPARQL, RDF, OWL) and harmonization of data
• Application experience of CI/CD implementations using git and a common CI/CD stack (e.g., Jenkins, CircleCI, GitLab, Azure DevOps)
• Experience with agile software development environments using tools like Jira and Confluence
• Experience with Infrastructure as a Code and automation tools (i.e. Terraform)

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