Junior Data Scientist

Jobtailor; Córdoba; Presencial; ARS 446.400 - 669.600; A tiempo parcial

Descripción de la vacante

Jobtailor in Córdoba is seeking a student data analytics intern to help define hypotheses and develop tests based on available data. You will work on understanding data sources, ensure data quality, and assist in feature extraction schema definitions.

The role emphasizes collaboration, learning new technologies, and effective communication of analytic results to the team. Intermediate English is expected; on-site work in Córdoba, Argentina is typical.

Formación

• Currently pursuing a degree in Computer Science, Statistics, Mathematics or other quantitative fields.
• Proficiency with Python.
• Basic knowledge 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.
• Exposure to project/task management platforms (e.g., JIRA, Trello).
• Familiarity with Git version control.
• Understanding of testing methodologies (e.g., pytest).
• Awareness of virtualization concepts, such as Docker.

Responsabilidades

• 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.

Conocimientos

Python

English

Data visualization

Machine learning concepts

Time series

Communication

Learning agility

Educación

Pursuing degree in Computer Science, Statistics, Mathematics

Herramientas

Pandas

Numpy

Matplotlib

Seaborn

GGPlot

Scikit-learn

XGBoost

Keras

Linux

Git

Docker

JIRA

Trello

pytest

Descripción del empleo

Responsibilities

• 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

• 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.