Senior Applied Data Scientist - ML, NLP & LLMs

Applaudo; Región Metropolitana de Santiago; On-site; CLP 24,000,000 - 42,000,000; Full time

Job summary

Applaudo is seeking an experienced Data Scientist to lead applied ML initiatives, including embedding-based matching and LLM-driven approaches. You will tackle messy, large-scale data, implement robust evaluation, and communicate trade-offs to stakeholders.

The role requires autonomy, strong experimentation discipline, and the ability to balance model quality with cost and scalability in real-world projects.

Qualifications

• 5+ years of professional Data Science / ML experience.
• Strong applied ML fundamentals with Python and SQL.
• Hands-on with embeddings and semantic similarity.
• Practical experience applying LLMs to real-world problems.
• Experience with supervised and unsupervised learning.
• Strong NLP, classification, and neural network basics.
• Awareness of scalable ML and inference cost considerations.
• Ability to design, run, and document experiments.

Responsibilities

• Build and evaluate ML approaches for company/entity matching.
• Develop embedding and LLM-based matching approaches.
• Create scoring/ranking to identify true matches and duplicates.
• Work with messy, multilingual data and firmographic attributes.
• Define benchmarks, metrics, baselines, and error-analysis workflows.
• Design experiments to validate hypotheses and compare approaches.
• Assess inference cost and scalability from the start.
• Communicate results to engineering and business stakeholders.

Skills

Python

SQL

Embeddings

NLP

LLMs

Experiment design

Model evaluation

Production ML

TensorFlow

PyTorch

Tools

TensorFlow

PyTorch

PyCaret

Spark

Snowflake

Databricks

BigQuery

Job description

Applaudo is seeking an experienced Data Scientist to lead applied ML initiatives, including embedding-based matching and LLM-driven approaches. You will tackle messy, large-scale data, implement robust evaluation, and communicate trade-offs to stakeholders.

The role requires autonomy, strong experimentation discipline, and the ability to balance model quality with cost and scalability in real-world projects.