Data & Analytics Engineer (AI/ML)

TL;DR

Data & Analytics Engineer (AI/ML): Implementing AI/ML solutions for demand forecasting and resource optimization in the public transportation industry with an accent on time-series forecasting and feature engineering. Focus on building classical ML models, developing data pipelines on AWS, and creating operational dashboards for capacity modeling.

Location: Remote (Must be based in Colombia)

Company

hirify.global is a tech transformation specialist that unites human expertise with AI to create scalable tech solutions for over 1,000 global clients.

What you will do

• Conduct exploratory data analysis (EDA) and statistical profiling to identify trends and insights.
• Build classical ML models for time-series forecasting, regression, and capacity/throughput modeling.
• Extract and transform data from relational databases (RDS, Oracle, PostgreSQL) into analytics-ready formats.
• Develop data ingestion and processing pipelines.
• Create data visualizations and dashboards using Amazon QuickSight or equivalent BI tools.
• Apply SHAP or other model explainability techniques to interpret model outputs.

Requirements

• 4+ years of experience in data engineering or applied data science roles, preferably with AWS.
• Proficiency in EDA, statistical profiling, and feature engineering for time-series forecasting.
• Strong understanding of classical ML modeling and evaluation metrics (RMSE, MAE, MAPE).
• Hands-on experience with Amazon SageMaker and the Python data stack (pandas, NumPy, scikit-learn).
• Working knowledge of SQL and dimensional modeling.
• Must be based in Colombia.

Nice to have

• Familiarity with SHAP or model explainability techniques.
• AWS Certifications: Cloud Practitioner, Data Engineer Associate, or Machine Learning Specialty.

Culture & Benefits

• Premium Healthcare and Life insurance.
• Meal vouchers and mobile services subsidy.
• Maternity and Parental leaves, as well as paid vacations.
• Continuous learning opportunities through hirify.global University.
• Inclusive work environment valuing diverse identities and life experiences.