Senior Applied Data Scientist - Scalable Entity Matching

gt-hq; Poland; On-site; PLN 180,000 - 280,000; Full time

Job summary

GT is seeking a Senior Applied Data Scientist to advance entity resolution at scale. You will develop and test ML, embedding, and LLM-based approaches for matching complex business records across data sources.

Collaboration with engineering will productionize successful methods, and you will explore foundation-model techniques for scalable improvement. You will evaluate match quality with precision/recall metrics, and drive experiments, baselines, and benchmarks while managing cost and latency

Qualifications

• 5-8 years of relevant experience in Data Science or Applied ML.
• Strong applied ML fundamentals with hands-on model evaluation on real data.
• Excellent Python and SQL skills.
• Experience with embeddings, semantic similarity, LLMs, or related AI techniques.
• Hands-on experience training supervised and unsupervised models, including NLP tasks.
• Knowledge of neural networks and transformer architectures.
• Proficiency with ML frameworks such as TensorFlow, PyTorch, PyCaret.
• Experience retraining taxonomy classifiers or production models.
• Ability to define baselines, metrics, and error analyses to show quality improvements.
• Ability to explain model behavior and tradeoffs to engineers and business partners.

Responsibilities

• Develop and test ML, embedding, and LLM approaches for entity matching at scale.
• Improve handling of messy data: multilingual records, aliases, domains, firmographics.
• Develop scoring/ranking to distinguish accurate matches from duplicates.
• Evaluate AI/ML techniques balancing accuracy, scalability, and cost.
• Design scalable approaches considering model usage and compute costs.
• Improve evaluation, experimentation, and match quality metrics.
• Build benchmark sets to compare models before production rollout.
• Explore LLM-assisted review to benchmark scalable solutions.
• Turn ambiguous matches into clear hypotheses, experiments, metrics, and recommendations.
• Collaborate with engineering to productionize successful ideas.
• Work with data and software engineers to turn prototypes into production logic.
• Provide model specs and evaluation results to stakeholders.
• Determine appropriate matching techniques based on data characteristics and cost.
• Continuously evaluate performance and investigate regressions.
• Communicate tradeoffs related to performance, scalability, cost, and latency.

Skills

Python

SQL

Embeddings

LLMs

NLP

TensorFlow

PyTorch

PyCaret

Experimentation

Model deployment

Tools

TensorFlow

PyTorch

PyCaret

Spark

BigQuery

Job description

GT is seeking a Senior Applied Data Scientist to advance entity resolution at scale. You will develop and test ML, embedding, and LLM-based approaches for matching complex business records across data sources.

Collaboration with engineering will productionize successful methods, and you will explore foundation-model techniques for scalable improvement. You will evaluate match quality with precision/recall metrics, and drive experiments, baselines, and benchmarks while managing cost and latency