Data and Machine Learning Intern

This is a fully remote position, open to applicants in Colombia.

📋 Description

• Assist in the design, development, and maintenance of data pipelines to guarantee clean, reliable, and timely data.

• Collaborate with the team to implement and enhance ETL processes.

• Integrate data from multiple sources into warehouses, data lakes, and lakehouses.

• Support data management activities, including data cleaning, validation, and transformation.

• Comprehend business objectives and create models that facilitate their achievement, along with metrics to monitor progress.

• Implement machine learning systems utilizing classical ML, deep learning, and foundation models while adhering to best practices.

• Engage in client communications by assisting in gathering requirements and conveying deliverables.

• Explore and visualize data with a keen eye for issues that necessitate data cleaning, as well as discrepancies in data distribution that may impact performance post-deployment.

• Identify and analyze errors in models.

⛳️ Requirements

• Final year of a bachelor’s degree in Computer Science or a related field.

• Proficient in English.

• Basic knowledge of Python, machine learning, and data libraries.

• Fundamental understanding of databases.

• Familiarity with statistical, machine learning, and deep learning algorithms.

• Experience in visualizing and manipulating large datasets.

• Strong problem-solving skills.

• Bonus: Knowledge of AWS, (Py)Spark, Airflow, Data Lakes, and Data Warehouses.

• Curiosity: A strong desire to learn and grow across various industries utilizing a modern tech stack.

• Autonomy and positivity: We operate as a fully remote, globally distributed team.

• Teamwork: Enjoy working collaboratively.

• Adaptability: Function with a startup mindset and maintain a startup pace.

• Dependability: Capable of delivering high-quality work consistently.

• Please submit your CV in English.

🏝️ Benefits

• Every other Friday off.

• Health bonus.

• Remote and flexible working arrangements.

• Paid sick days and local holidays.