[Job-29268] Machine Learning Engineering, Colombia

ColombiaRemoteHomeofficemid

Machine LearningData & AI

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Quick Summary

Key Responsibilities

Exploratory Data Analysis (EDA): Conduct EDA and statistical profiling to identify trends and insights from data. Perform feature engineering specifically for time-series forecasting.

Requirements Summary

4+ years in data engineering or applied data science roles, preferably with experience on AWS. Proficient in exploratory data analysis (EDA), statistical profiling,

Technical Tools

Machine LearningData & AI

We are tech transformation specialists, uniting human expertise with AI to create scalable tech solutions.

With over 8,000 CI&Ters around the world, we’ve built partnerships with more than 1,000 clients during our 30 years of history. Artificial Intelligence is our reality.

We are looking for a Data & Analytics Engineer supporting an AI/ML implementation for demand forecasting and resource optimization in the public transportation / fare collection industry. Works alongside an AWS ProServe ML Specialist on EDA, feature engineering, capacity modeling, and operational dashboards.

Responsibilities:

Conduct EDA and statistical profiling to identify trends and insights from data.
Perform feature engineering specifically for time-series forecasting.

Extract and transform data from relational databases (RDS, Oracle, PostgreSQL) into analytics-ready formats.
Develop pipelines for data ingestion and processing.

Build classical ML models for time-series forecasting, regression, and capacity/throughput modeling.
Evaluate model performance using metrics such as RMSE, MAE, and MAPE, documenting performance results.

Create insightful data visualizations and dashboards using Amazon QuickSight or equivalent BI tools.

Utilize the Python data stack (pandas, NumPy, scikit-learn, matplotlib/seaborn) for data manipulation and analysis.

Apply SHAP or other model explainability techniques to interpret model outputs.

Work closely with stakeholders to translate business rules into effective feature engineering pipelines.
Engage in milestone-driven, Firm Fixed Price delivery models, ensuring timely project completion.

Requirements

~1 min read

4+ years in data engineering or applied data science roles, preferably with experience on AWS.
Proficient in exploratory data analysis (EDA), statistical profiling, and feature engineering for time-series forecasting.
Experience in data wrangling from relational databases (RDS, Oracle, PostgreSQL) into analytics-ready formats.
Strong understanding of classical ML modeling techniques, including time-series forecasting and regression.
Familiarity with model evaluation metrics (RMSE, MAE, MAPE) and performance documentation.
Experience in data visualization and dashboard development using Amazon QuickSight or equivalent BI tools.
Hands-on experience with Amazon SageMaker (training, evaluation, Clarify).
Proficient in the Python data stack, including pandas, NumPy, scikit-learn, matplotlib, and seaborn.
Working knowledge of SQL and dimensional modeling.
Familiarity with SHAP or model explainability techniques is a plus.

Expected Certifications

AWS Certified Cloud Practitioner
AWS Certified Data Engineer – Associate
AWS Certified Machine Learning – Associate or AWS Certified Machine Learning – Specialty

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