Data Engineering Leader and NLP Strategist

2 weeks ago

London, Greater London, United Kingdom GlaxoSmithKline Full time

GlaxoSmithKline's Research Data Tech organization, Onyx, is a cutting-edge data ecosystem that brings together, analyzes, and powers the exploration of data at scale. The goal is to provide scientists with the right data and insights when they need it, accelerating medical discovery and helping us get ahead of disease in more predictive and powerful ways.

Onyx is a full-stack shop consisting of product and portfolio leadership, data engineering, infrastructure and DevOps, data / metadata / knowledge platforms, and AI/ML and analysis platforms. Our team is responsible for designing, delivering, supporting, and maintaining industrialized automated end-to-end data services and pipelines.

We are seeking an experienced Data Engineering Leader and NLP Strategist to build and lead a scrum team of world-class NLP data engineers focused on building automated, scalable, and sustainable pipelines to account for evolving scientific needs. You will 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.

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 decisions, anticipate future issues, and drive engineering efficiencies
  • Partner with AIML and knowledge graph platform team to build, test, and deploy NLP pipelines, systems, and solutions
  • Partner closely with other data engineering leads to conceptualize the design of new data flows aimed at maximizing reuse and aligning with an event-driven microservice enable architecture
  • Partner with other data engineering leads to architect an engagement model and optimal ways of working with the product management teams
  • 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
  • Create standards for proper ways of working and engineering discipline, including the QMS framework and CI/CD best practices and proactively spearhead improvement within their engineering area
  • Exemplar leader in their field of technical knowledge, keen on bettering their understanding and acting as the knowledge holder for the organization
Requirements:
  • 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:
  • Demonstrable 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., li>
  • 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.

The estimated salary for this role is between $140,000 - $200,000 per year, depending on location and experience.

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