C
Ciandtvia Lever
[Job-29268] Machine Learning Engineering, Colombia
REMOTEPosted 6d ago
ML EngineerMid LevelFull-time
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About the Role
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:
Exploratory Data Analysis (EDA):
Conduct EDA and statistical profiling to identify trends and insights from data.
Perform feature engineering specifically for time-series forecasting.
Data Wrangling and Preparation:
Extract and transform data from relational databases (RDS, Oracle, PostgreSQL) into analytics-ready formats.
Develop pipelines for data ingestion and processing.
Machine Learning Modeling:
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.
Data Visualization:
Create insightful data visualizations and dashboards using Amazon QuickSight or equivalent BI tools.
Python Data Stack:
Utilize the Python data stack (pandas, NumPy, scikit-learn, matplotlib/seaborn) for data manipulation and analysis.
Model Explainability:
Apply SHAP or other model explainability techniques to interpret model outputs.
Collaboration and Communication:
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 for this challenge:
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
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:
Exploratory Data Analysis (EDA):
Conduct EDA and statistical profiling to identify trends and insights from data.
Perform feature engineering specifically for time-series forecasting.
Data Wrangling and Preparation:
Extract and transform data from relational databases (RDS, Oracle, PostgreSQL) into analytics-ready formats.
Develop pipelines for data ingestion and processing.
Machine Learning Modeling:
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.
Data Visualization:
Create insightful data visualizations and dashboards using Amazon QuickSight or equivalent BI tools.
Python Data Stack:
Utilize the Python data stack (pandas, NumPy, scikit-learn, matplotlib/seaborn) for data manipulation and analysis.
Model Explainability:
Apply SHAP or other model explainability techniques to interpret model outputs.
Collaboration and Communication:
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 for this challenge:
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
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