At a Glance
- Tasks: Lead the optimisation and scaling of our biomedical data infrastructure for AI applications.
- Company: Innovative tech firm in London with a hybrid work model.
- Benefits: Competitive pay, flexible working, and opportunities for professional growth.
- Other info: Join a dynamic team focused on advancing healthcare through technology.
- Why this job: Make a real impact by bridging complex data with cutting-edge AI technologies.
- Qualifications: 10+ years in graph databases and experience with biomedical data.
The predicted salary is between 80000 - 100000 £ per year.
Location: London, Hybrid
Type of employment: Inside IR35 contract
Role Overview: We are seeking a Knowledge Graph Lead Engineer to evaluate, optimize, and scale our enterprise semantic data infrastructure. In this role, you will bridge the gap between complex biomedical data and actionable AI applications. You will conduct comprehensive health checks on our current stack, refine bio-ontologies, and build the strategic roadmap for our next-generation GraphRAG and GenAI platforms.
Key responsibilities:
- Platform Assessment: Conduct comprehensive technical health checks on existing graph databases and cluster infrastructure.
- Ontology & Schema Review: Evaluate RDF/OWL and Labeled Property Graph (LPG) schemas for enterprise scalability.
- Standards Alignment: Map internal data frameworks to biomedical standards like MeSH, SNOMED, and UMLS.
- Performance Optimization: Eliminate data bottlenecks across real-time ingestion pipelines and complex query execution.
- Strategic Roadmap: Author comprehensive "Way Forward" reports detailing cloud migration and build-vs-buy decisions.
- AI & LLM Integration: Design infrastructure to connect knowledge graphs with Large Language Models using GraphRAG frameworks.
- Stakeholder Alignment: Translate technical graph concepts into clear business value for Research and Clinical teams.
Skills:
- Graph Expertise: 10+ years of engineering experience with Graph Databases, Triple Stores, or Labeled Property Graphs.
- Technical Stack Preferences:
- Graph Databases: Stardog, AnzoGraph, or Neo4j
- Query & Programming Languages: SPARQL, Cypher, Gremlin, Python, and Java
- AI Tools: Any Vibe Coding Tool (Claude Code OR GHCP)
- Pharma Domain Knowledge:
- Proven track record handling biomedical data like gene-disease associations and chemistry structures.
- Experience modeling Chemistry Manufacturing and Control data types, including product journeys and electronic data capture logs.
- OBO Foundry, ChEMBL, Ensembl, and Monarch Initiative
- Advanced AI/GenAI: Hands-on experience designing and executing Graph RAGs, Context Graphs, Agents
- Semantic Web Standards: Deep understanding of W3C standards, Linked Data principles, and URI minting strategies.
StudySmarter Expert Advice🤫
We think this is how you could land Lead Knowledge Graph Engineer in Watford
✨Showcase Your Skills with a Public Portfolio
As a freelancer in data science, having a killer portfolio is essential. Showcase your projects on platforms like GitHub or create a personal website that details your work and techniques. This gives potential clients a clear picture of what you can do and helps you stand out from the competition.
✨Get Involved in Data Science Communities
Tap into online forums like Kaggle or Stack Overflow. Not only can you showcase your expertise, but you can also connect with other data scientists and potential clients. Plus, participating in competitions and discussions can elevate your profile in the field.
✨Leverage Local Networking Opportunities
Keep an eye out for local data science meetups or tech events in your area. These are golden opportunities to meet potential clients and collaborators face-to-face. Plus, who doesn't love a bit of networking over pizza and drinks?
✨Pitch Your Services Directly to Companies
Don't just wait for freelancing platforms to bring clients to you—be proactive! Research companies that could benefit from data science services and craft tailored pitches. Mention specific pain points you can address for them. Let’s get that freelance hustle going!
We think you need these skills to ace Lead Knowledge Graph Engineer in Watford
Some tips for your application 🫡
Showcase Your Projects:When applying for a freelance data science role like Lead Knowledge Graph Engineer at Upbeat Ideas UK Ltd, it’s crucial to highlight your projects. Include a portfolio that features at least two or three projects involving data analysis, machine learning, or visualisation. Make sure to describe the tools and methodologies you used, so we can see your skills in action!
Quantify Your Achievements:Freelance gigs, especially in data science, often ask for proven results. In your CV, include any relevant metrics or outcomes from your previous work. Did your analysis help reduce costs by a certain percentage? Or did your predictive model improve performance? Numbers speak volumes!
Introduce Your Style:Since freelancing is all about your individual style and approach, use your cover letter to share how you tackle data problems. This is your chance to let us know how you think, your creative problem-solving methods, and how you would approach a project at Upbeat Ideas UK Ltd.
Be Real About Your Rates:When you send in your application, don’t forget to mention your freelance rates and availability. We appreciate clarity up front, and it helps us gauge if you fit within our budget and timeline. Being transparent in this aspect shows professionalism and readiness!
How to prepare for a job interview at Upbeat Ideas UK Ltd
✨Show Off Your Data Wizardry
As a freelancer in data science, you'll want to present a portfolio that showcases your best projects. We should pull together examples where you tackled real problems with data analytics, machine learning models, or visualisations. It's all about demonstrating your skills in action!
✨Be Ready to Dive Deep into Technical Questions
Expect to encounter some technical grilling during the interview. Prepare to discuss statistical methods, algorithms, or maybe even tackle a live coding challenge. We should brush up on tools like Python, R, or SQL—those are key players in the data science field. Don't just know them; be ready to explain your thought process!
✨Help Them Understand Your Work Style
Freelance gigs often mean you'll be working independently, so we need to convey our self-motivation and time management skills. Be prepared to talk about how you’ve handled multiple projects or met tight deadlines before. Sharing your approach to client communication can also give them confidence in your ability to deliver remotely.
✨Pitch Your Value Proposition
When freelancing, it’s crucial to clearly articulate what makes you unique. We should highlight not just technical skills but also the business impact of our projects. Think of a couple of stories where your data insights drove decision-making—this can be a game changer in showing why they should choose you for their freelance needs!
