Senior Applied Data Scientist | NDA

GT was founded in 2019 by a former Apple Nest and Google executive.GTs mission is to connect the worlds 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.

Our client is a leading global management consultancy known for tackling some of the worlds 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.

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.

Develop better ways to match company records

• Build new ML embedding and LLM-based approaches for matching entities

• Improve how the system handles messy data including name variations aliases domains websites firmographic attributes multilingual records and data hierarchies.

• Develop scoring and ranking approaches to distinguish accurate matches from duplicates similar-looking records and unrelated entities.

• Evaluate and implement AI and machine learning techniques to improve matching quality while considering accuracy scalability and cost.

• Design approaches that can operate efficiently at scale taking model usage and computational cost into consideration.

Improve evaluation experimentation and match quality

• Define and improve methods for evaluating match quality including precision recall false positives false negatives confidence coverage and manual review effort.

• Assist in building trusted benchmark sets that allow us to compare new models against the current matching engine before production rollout.

• Explore LLM-assisted review and validation to assess matching performance and benchmark more scalable approaches.

• Turn ambiguous matching problems into clear hypotheses experiments metrics and recommendations.

Partner with engineering to bring successful ideas into production

• Work closely with data engineering and software engineering teams to turn promising prototypes into production-ready matching logic.

• Provide engineering partners with clear model specifications evaluation results expected behavior edge cases and rollout requirements.

• Help determine the most appropriate matching techniques based on data characteristics confidence levels and cost considerations.

• Continuously evaluate matching performance investigate regressions and recommend improvements to models and matching logic.

• Clearly communicate technical tradeoffs related to matching performance scalability cost latency explainability and operational considerations.

• 58 years of relevant experience in Data Science Applied Data Science Applied Machine Learning or a similar role.

• Strong applied ML fundamentals with hands-on experience building and evaluating models on real data.

• Excellent Python and SQL skills.

• Practical experience with embeddings semantic similarity LLMs or related AI techniques.

• Hands-on experience training supervised and unsupervised models including classification and NLP tasks.

• Working knowledge of neural network and transformer architectures.

• Proficiency with common ML frameworks such as TensorFlow PyTorch and PyCaret.

• Experience retraining a taxonomy classifier or maintaining classification models in production.

• Experimental judgment: able to define baselines metrics test sets and error analysis that show whether quality improved.

• Ability to explain model behavior tradeoffs and edge cases clearly to engineering and business partners.

• Experience with entity resolution record linkage deduplication or similar matching problems.

• Experience with ranking similarity scoring retrieval clustering or candidate generation.

• Experience applying LLMs or embeddings to business problems where cost and scale matter.

• Exposure to large-scale data platforms such as Spark Snowflake Databricks or BigQuery.

• Familiarity with company domain website firmographic or other business-entity data.

1. GT interview with Recruiter

2. Technical interview

3. Final interview

Required Experience:

Senior IC