Data Scientist – Machine Learning & LLMs

About the role

Applaudo is seeking an experienced Data Scientist to lead the design, development, and evaluation of machine‑learning solutions for large‑scale, messy entity‑matching problems. You will work with real‑world data, embeddings, and large language models to create cost‑effective, scalable matching pipelines.

Key responsibilities

• Build and evaluate ML models for company/entity matching, including embedding‑ and LLM‑based approaches.
• Design scoring and ranking methodologies to differentiate true matches from duplicates and unrelated entities.
• Handle noisy data such as names, aliases, domains, firmographic attributes, and multilingual records.
• Define benchmark datasets, metrics, baselines, and error‑analysis processes.
• Run structured experiments, compare LLM‑assisted methods with lower‑cost alternatives, and assess inference economics.
• Communicate findings and recommendations to engineering and business stakeholders.
• Establish and document experimental pipelines, including 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 of working with large, messy datasets.
• Highly autonomous, intellectually honest, and able to communicate technical trade‑offs clearly.
• Excellent English communication skills.

Required skills

• Python and SQL.
• TensorFlow, PyTorch, or equivalent ML frameworks (e.g., PyCaret).
• Experience with embeddings, semantic similarity, LLMs, NLP, and classification.
• Hands‑on work with neural networks and transformer architectures.
• Familiarity with Spark, Snowflake, Databricks, or BigQuery.