Lead Data Scientist, Adtech & Bid Optimization

Launch Potato; Quilmes; On-site; ARS 1,800,000 - 3,000,000; Full time

Job summary

Launch Potato is seeking a Machine Learning Engineer with a strong background in Python and NLP to build document understanding and data processing solutions for fintech use cases.

You will develop models, implement fuzzy matching, and deploy REST APIs with Docker and Azure ML, collaborating with cross-functional teams to meet project goals.

Qualifications

• Proficient in Python and ML libraries (Pandas, NumPy, scikit-learn).
• Experience with NLP libraries (spaCy, NLTK, transformers).
• Knowledge of fuzzy matching algorithms.
• Document processing experience (OCR, PDF parsing, Excel).
• ML model deployment experience (REST APIs, Docker, Azure ML).
• Strong understanding of supervised and unsupervised learning.
• Experience with deep learning frameworks (TensorFlow, PyTorch).
• Experience with LLMs for document understanding (OpenAI, Azure OpenAI).
• Knowledge of Azure ML services and Cognitive Services.
• 2–5 years of ML engineering experience in NLP or financial data processing.
• Experience in finance/fintech domain (reconciliation, accounting).

Responsibilities

• Develop and implement ML models for document understanding and processing.
• Utilize Python libraries such as Pandas, NumPy, and scikit-learn for data manipulation and analysis.
• Apply NLP techniques using libraries like spaCy, NLTK, or transformers.
• Implement fuzzy matching algorithms (Levenshtein distance, record linkage) for data accuracy.
• Process documents using OCR, PDF parsing, and Excel data extraction methods.
• Deploy machine learning models as REST APIs using Docker and Azure ML.
• Understand and apply supervised and unsupervised learning techniques.
• Design and manage data pipelines and ETL processes.
• Collaborate with cross-functional teams to meet project goals.

Skills

Python programming

NLP techniques

Fuzzy matching

Document processing

ML model deployment

Supervised learning

Unsupervised learning

Deep learning

LLMs for docs

Education

Master’s in CS/Data Science

Tools

Docker

Azure ML

spaCy

NLTK

Transformers

TensorFlow

PyTorch

OpenAI

Azure OpenAI

OCR

PDF parsing

Excel extraction

Job description

We are seeking a Machine Learning Engineer with strong expertise in Python and a focus on Natural Language Processing (NLP) to join our team. The ideal candidate will have experience in the finance or fintech domain, specifically in reconciliation and accounting.

Responsibilities

• Develop and implement machine learning models for document understanding and processing.
• Utilize Python libraries such as Pandas, NumPy, and scikit-learn for data manipulation and analysis.
• Apply NLP techniques using libraries like spaCy, NLTK, or transformers.
• Implement fuzzy matching algorithms (Levenshtein distance, record linkage) for data accuracy.
• Process documents using OCR, PDF parsing, and Excel data extraction methods.
• Deploy machine learning models as REST APIs using Docker and Azure ML.
• Understand and apply supervised and unsupervised learning techniques.
• Design and manage data pipelines and ETL processes.
• Collaborate with cross-functional teams to meet project goals.

Required Skills

• Strong expertise in Python (Pandas, NumPy, scikit-learn).
• Experience with NLP libraries (spaCy, NLTK, transformers).
• Knowledge of fuzzy matching algorithms.
• Document processing skills (OCR, PDF parsing, Excel extraction).
• Experience in ML model deployment (REST APIs, Docker, Azure ML).
• Understanding of supervised/unsupervised learning techniques.
• Familiarity with deep learning frameworks (TensorFlow, PyTorch).
• Experience with Large Language Models (LLMs) for document understanding (OpenAI, Azure OpenAI).
• Knowledge of Azure ML services and Azure Cognitive Services.
• 2-5 years of experience in ML engineering, focusing on NLP or financial data processing.
• Experience in the finance/fintech domain (reconciliation, accounting).

Education

Master’s degree in Computer Science, Data Science, or a related field.