Senior Applied Data Scientist | NDA

The role

You will develop and test new ML, embedding, and LLM-based approaches to solve complex data matching problems at scale. The work focuses on improving entity resolution, model quality, experimentation, and evaluation.

What you will do

• 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
• Design approaches that can operate efficiently at scale
• Define and improve methods for evaluating match quality
• Assist in building trusted benchmark sets
• Explore LLM-assisted review and validation
• Turn ambiguous matching problems into clear hypotheses, experiments, metrics, and recommendations
• Work closely with data engineering and software engineering teams to turn promising prototypes into production-ready matching logic

Required qualifications

• 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
• 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

Before you apply

• 1. GT interview with Recruiter
• 2. Technical interview
• 3. Final interview

About the company

GT's mission is to connect the world’s best talent with product careers offered by high-growth companies. The client is a leading global management consultancy known for tackling complex business challenges.