Neil Sinclair
Senior Data Scientist @diconium
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WORK HISTORY
Senior Data Scientist @diconium
Berlin, DE
Built a multi-agent voice and text sales assistant for a leading EU auto brand using OpenAI and deployed to Azure. The agents operated asynchronously in a hierarchical structure with dynamic routing, RAG-driven content generation, structured information extraction, and Salesforce API integration- Led a team prototyping a RAG-based IoT data ordering system for analysts deployed on Databricks with Llama2. Data preprocessing incorporated LLM-enhanced data augmentation. The user-facing tool consisted of a multi-agent pipeline with query restructuring, RAG, and multi-step response writing- Deployed a multi-agent after-sales service assistant on Databricks using Llama2. The tool identifies correct repair procedures based on vehicle error signals via RAG, LLM-enhanced document selection and conversational document querying- Developed and deployed an intelligent pricing engine across multiple VW brands and EU markets.
EDUCATION
University of Cape Town
Master of Science - MS, Data Science
GIBS Business School (Gordon Institute of Business Science)
Master of Business Administration (MBA), Business Administration and Management, General
GIBS Business School (Gordon Institute of Business Science)
Post Graduate Diploma in General Management, Business Administration and Management, General
University of Cape Town
B.Soc.Sci, Sociology & Psychology
SKILLS
ABOUT NEIL SINCLAIR
I\'m an ex-Deloitte strategy consultant and experienced data scientist and AI engineer with a track record of deploying multi-agent LLM tools to the cloud. Throughout my career I’ve worked with some of the largest firms in Africa and Europe helping them solve their data and decision challenges.I work best where roles overlap: between strategy, data, and product. I like to move fast, learn fast, and build things that solve problems. Too often, data science produces elegant models that don\'t speak to real needs. My time as a strategy consultant taught me that context and customers matter more than theory. That perspective drives how I approach data science, starting with defining the problem and developing the solution from that grounding.
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