Data Scientist – AI and NLP

Role Overview

The role involves working as a Data Scientist in the AI and NLP team, developing advanced data mining solutions, and identifying profitable opportunities. The candidate will be responsible for building strong relationships with clients, monitoring project progress, and providing excellent client service.

What You Will Do

The main responsibilities include collecting business requirements, developing data mining solutions, analyzing user needs, and contributing to the implementation of the identified architecture. The candidate will also be responsible for scripting and programming, data modeling, and writing documentation.

Why It Might Be a Fit

The ideal candidate should have a Master’s degree and 3 years of relevant experience, with expertise in statistical computing, data visualization, and machine learning. The candidate should also have experience in scripting, database architecture, and query development, as well as knowledge of Python, R, and SQL.

Requirements

• Master Degree + 3 years of relevant professional experience
• At least 3 years of experience in statistical computing and data visualization using R, SAS, SPSS, STATA or equivalent platforms
• At least 2 years of experience in scripting and programming using Python and JavaScript
• At least 2 years of experience in database architecture and query development, covering SQL and NoSQL technologies such as MySQL, PostgreSQL, Microsoft SQL Server, MongoDB and Elasticsearch
• At least 1 year of experience in using Python to collect, clean, process and analyze structured and unstructured data, with libraries such as pandas and NumPy or equivalent tools
• Applying machine learning and natural language processing (NLP) to textual data for information extraction, document classification, clustering and semantic similarity, using tools such as spaCy, scikit-learn and Sentence Transformers or equivalent technologies
• Assessing data quality and validating analytical outputs, including comparison with human-reviewed examples, error analysis, and documentation of uncertainty and limitations
• Identifying trends and patterns, synthesizing information from multiple sources, and preparing analytical summaries, reports or briefs in English, with accurate referencing and clear presentation of findings