Junior Data Scientist

Fligoo

Data Scientist Junior responsible for learning data science methodologies at Fligoo. Collaborating with experienced data scientists to address business challenges in AI technology.

Posted 6/25/2026full-timeCórdoba • 🇦🇷 ArgentinaJuniorWebsite

Tech Stack

Tools & technologies

DockerKerasLinuxMacOSNumpyPandasPythonScikit-Learn

About the role

Key responsibilities & impact

Contribute to defining hypotheses and proposing analytic approaches based on available data.
Collaborate on the development and execution of tests to validate hypotheses.
Work on understanding data sources and extracting relevant information.
Collaborate to perform data quality checks to ensure the effectiveness and reliability of data.
Learn the definition of feature extraction schemas in collaboration with the team.
Contribute to communicate findings and results through effective data storytelling resources.
Assist in validating running solutions through hypothesis testing.

Requirements

What you’ll need

Currently pursuing a degree in Computer Science, Statistics, Mathematics or other quantitative fields
Proficiency with Python
Basic understanding of Python libraries: Pandas, Numpy, Matplotlib, Seaborn, GGPlot.
Knowledge of machine learning libraries: Scikit-learn, XGBoost, Keras.
Exposure to data visualization tools and frameworks.
Familiarity with Linux/MacOS/Windows operating systems.
Exposure to project task management platforms (e.g., JIRA, Trello).
Familiarity with code versioning using systems like Git.
Understanding of testing methodologies (e.g., pytest).
Awareness of virtualization concepts, such as Docker.
Developing effective communication skills, both written and verbal, especially in discussing technical subjects.
Demonstrating a strong interest and aptitude for learning new technologies and methodologies.
Intermediate English proficiency, enabling participation in discussions on technical topics.
Understanding of supervised and unsupervised learning concepts.
Exposure to tabular data, time series, and tree-based algorithms (e.g., Random Forest, Gradient Boosting Machines).
Exposure to communication tasks related to presenting analytic results.
Familiarity or exposure to descriptive, diagnostic and prescriptive analytics.
Understanding of correlation analysis techniques (e.g., scatter plots, correlation matrices).
Basic familiarity with time series plots and variable importance.
Exposure to A/B testing campaigns is a plus.

Benefits

Comp & perks

Professional development opportunities

ATS Keywords

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Hard Skills & Tools

PythonPandasNumpyMatplotlibSeabornGGPlotScikit-learnXGBoostKerastesting methodologies

Soft Skills

effective communicationdata storytellinglearning aptitudewritten communicationverbal communication