Data Scientist (Machine Learning & NLP)

About the role

Applaudo is looking for an experienced Data Scientist with deep expertise in applied machine learning, especially in embeddings, semantic similarity, large language models (LLMs), and natural language processing (NLP). You will tackle ambiguous, large‑scale data problems and turn them into measurable, production‑ready solutions.

Key responsibilities

• Design, build, and evaluate ML models for company/entity matching using embeddings and LLMs.
• Develop scoring and ranking methodologies to differentiate true matches from duplicates, look‑alikes, and unrelated entities.
• Work with messy, multilingual data including names, aliases, domains, websites, and firmographic attributes.
• Define benchmark datasets, baselines, metrics, and error‑analysis processes.
• Design and execute experiments, compare LLM‑based approaches with lower‑cost alternatives, and assess inference economics.
• Communicate findings and recommendations to engineering and business stakeholders.
• Establish experimental pipelines, document both successful and unsuccessful experiments, and maintain models in production.

Required profile

• 5+ years of professional data science or machine‑learning experience.
• Strong applied ML fundamentals and a track record of delivering models on real‑world, large‑scale data.
• Excellent Python and SQL skills.
• Hands‑on experience with embeddings, semantic similarity, LLMs, and NLP.
• Experience with both supervised and unsupervised learning, classification, and neural‑network architectures.
• Proficiency with TensorFlow, PyTorch, or equivalent frameworks.
• Demonstrated ability to design experiments, define baselines, metrics, and conduct error analysis.
• Strong English communication skills and the ability to convey technical trade‑offs to non‑technical audiences.

Required skills

• Python
• SQL
• Embeddings & semantic similarity
• Large language models (LLMs)
• Natural language processing (NLP)
• Classification
• Supervised & unsupervised learning
• Neural networks & transformer architectures
• TensorFlow
• PyTorch
• PyCaret
• Experiment design & error analysis
• Scalability & inference cost evaluation