Data Scientist (ML & LLMs)

About the role

Applaudo is looking for an experienced Data Scientist to lead the design, development, and evaluation of machine‑learning solutions for entity matching and deduplication. You will work with large, messy, multilingual datasets and apply cutting‑edge embedding and large language model techniques to improve data quality and business outcomes.

Key responsibilities

• Build and evaluate ML models for company/entity matching, including embedding‑based and LLM‑driven approaches.
• Develop scoring and ranking methodologies to differentiate true matches from duplicates, look‑alikes, and unrelated entities.
• Handle noisy data such as names, aliases, domains, websites, and firmographic attributes.
• Define benchmark datasets, metrics, baselines, and error‑analysis processes.
• Design and execute experiments, compare LLM‑assisted solutions with lower‑cost alternatives, and assess inference economics and scalability.
• Communicate experimental findings and recommendations to engineering and business stakeholders.
• Establish end‑to‑end experimental pipelines and thoroughly document both successful and unsuccessful trials.

Required profile

• 5+ years of professional Data Science or Machine Learning experience.
• Strong applied ML fundamentals and a track record with real‑world, large‑scale data.
• Excellent Python and SQL skills.
• Hands‑on experience with embeddings, semantic similarity, and large language models.
• Practical experience in supervised and unsupervised learning, classification, and NLP.
• Working knowledge of neural networks, transformer architectures, and frameworks such as TensorFlow, PyTorch, or PyCaret.
• Experience maintaining and retraining classification models in production.
• Proven ability to design experiments, define baselines, metrics, test sets, and conduct error analysis.
• Strong English communication skills and the ability to convey technical trade‑offs to non‑technical audiences.

Required skills

• Python
• SQL
• TensorFlow
• PyTorch
• PyCaret
• Embeddings & semantic similarity
• Large language models (LLMs)
• Classification
• Natural language processing (NLP)
• Neural networks & transformer architectures