Applied Data Scientist
Posted 31 Aug 2026
Advertised as “Senior Applied Data Scientist | NDA”
BranchFactor Summary
The original posting with the fluff stripped out
- Expected experience
- 5–8 yrs
- Management level
- Individual contributor
- Employment
- Hybrid
- Contract
- Full-time
No salary listed for this role.
GT was founded in 2019 by a former Apple, Nest, and Google executive. GT’s mission is to connect the world’s best talent with product careers offered by high-growth companies in the UK, USA, Canada, Germany, and the Netherlands.
On behalf of our client, GT is looking for a Senior Applied Data Scientist interested in developing and testing new ML, embedding, and LLM-based approaches to solve complex data matching problems at scale.
About the Client
Our client is a leading global management consultancy known for tackling some of the world’s most complex business challenges. With a focus on strategy, transformation, and performance improvement, the firm partners with major organizations across industries to drive lasting impact.
About the Role
We are looking for a Senior Applied Data Scientist to improve how entity resolution is performed at scale.
You will develop and test new ML, embedding, and LLM-based approaches for matching complex business records across multiple data sources.
The work is centered on model quality, experimentation, and evaluation; engineering partners will help productionize successful approaches.
A key part of the role is exploring how newer foundation-model techniques can improve matching quality while remaining practical and scalable for very large datasets.
Responsibilities:
Develop better ways to match company records
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Build new ML, embedding, and LLM-based approaches for matching entities
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Improve how the system handles messy data, including name variations, aliases, domains, websites, firmographic attributes, multilingual records, and data hierarchies.
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Develop scoring and ranking approaches to distinguish accurate matches from duplicates, similar-looking records, and unrelated entities.
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Evaluate and implement AI and machine learning techniques to improve matching quality while considering accuracy, scalability, and cost.
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Design approaches that can operate efficiently at scale, taking model usage and computational cost into consideration.
Improve evaluation, experimentation, and match quality
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Define and improve methods for evaluating match quality, including precision, recall, false positives, false negatives, confidence, coverage, and manual review effort.
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Assist in building trusted benchmark sets that allow us to compare new models against the current matching engine before production rollout.