Senior Data Scientist: NLP, LLMs & Production ML

Applaudo; Peru (IL); On-site; USD 90,000 - 150,000; Full time

Job summary

Applaudo is seeking an experienced Data Scientist with strong applied machine learning expertise to build and evaluate models on real-world, large-scale data. You will work with embeddings, semantic similarity, LLMs, NLP, classification, and both supervised and unsupervised learning.

You will design experiments, define baselines and metrics, document results, and communicate trade-offs to engineering and business stakeholders while optimizing for scalability and cost.

Qualifications

• 5+ years of professional Data Science / Machine Learning experience.
• Hands-on experience with embeddings and semantic similarity.
• Practical experience applying LLMs to real-world problems.

Responsibilities

• Build and evaluate ML approaches for company/entity matching.
• Develop embedding and LLM-based matching approaches.
• Develop scoring and ranking methodologies to identify true matches and distinguish duplicates, lookalikes, and unrelated entities.
• Work with messy data, including names, aliases, domains, websites, firmographic attributes, multilingual records, and data hierarchies.
• Define benchmark datasets, metrics, baselines, and error-analysis processes.
• Design and execute experiments to validate hypotheses.
• Compare LLM-assisted approaches against lower-cost alternatives.
• Analyze model behavior, edge cases, and trade-offs.
• Consider inference economics and scalability from the beginning.
• Communicate experimental findings and recommendations to engineering and business stakeholders.
• Independently establish experimental pipelines and research approaches.
• Clearly document both successful and unsuccessful experiments.

Skills

Applied ML fundamentals

Python

SQL

Embeddings

LLMs

NLP

Classification

Experiment design

Model evaluation

Production ML

Tools

TensorFlow

PyTorch

Job description

Applaudo is seeking an experienced Data Scientist with strong applied machine learning expertise to build and evaluate models on real-world, large-scale data. You will work with embeddings, semantic similarity, LLMs, NLP, classification, and both supervised and unsupervised learning.

You will design experiments, define baselines and metrics, document results, and communicate trade-offs to engineering and business stakeholders while optimizing for scalability and cost.