Senior Applied Data Scientist | NDA

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

• 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.

Essential knowledge, skills & experience

• 5–8 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.

Nice-to-have

• 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.

Interview Steps

• GT interview with Recruiter
• Technical interview
• Final interview