Senior Data Scientist: ML & NLP for Real-World Matching

Applaudo; Chile; Sur place; CLP 87 891 000 - 126 953 000; Plein temps

Résumé du poste

Applaudo in Chile seeks an experienced Data Scientist with strong ML expertise to build and evaluate models on large-scale, messy data. You will work on embeddings, semantic similarity, LLMs, NLP, and both supervised and unsupervised learning, communicating results to stakeholders.

You will own experiments, define baselines, metrics, and error analyses, balancing model quality with cost and scalability. The role requires autonomy and collaboration across data, engineering, and product teams.

Qualifications

• 5+ years of professional Data Science / Machine Learning experience.
• Strong applied Machine Learning fundamentals.
• Excellent Python and SQL skills.
• Hands-on experience with embeddings and semantic similarity.
• Practical experience applying LLMs to real-world problems.
• Experience with supervised and unsupervised learning.
• Strong experience with classification and NLP.
• Working knowledge of neural networks and transformer architectures.
• Hands-on experience with TensorFlow, PyTorch, PyCaret, or equivalent ML frameworks.
• Experience retraining or maintaining classification models in production.
• Strong experimental design and model evaluation skills.
• Experience defining baselines, metrics, test sets, and error-analysis processes.
• Ability to evaluate model quality and demonstrate measurable improvements.
• Strong understanding of scalability and ML inference costs.

Responsabilités

• 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 them from 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.

Connaissances

Python

SQL

Embeddings

Semantic similarity

LLMs

NLP

Supervised learning

Unsupervised learning

Classification

Experiment design

Model evaluation

Scalability

Outils

TensorFlow

PyTorch

PyCaret

Spark

Snowflake

Databricks

BigQuery

Description du poste

Applaudo in Chile seeks an experienced Data Scientist with strong ML expertise to build and evaluate models on large-scale, messy data. You will work on embeddings, semantic similarity, LLMs, NLP, and both supervised and unsupervised learning, communicating results to stakeholders.

You will own experiments, define baselines, metrics, and error analyses, balancing model quality with cost and scalability. The role requires autonomy and collaboration across data, engineering, and product teams.