Job Description
AI & Data Engineer (AI/ML + Data Science Project-Based Consultant)
AI & Data Tech Team Technology Solutions & Delivery (TSD)
ASA Philippines Foundation
Company Description
ASA Philippines Foundation is one of the country's largest microfinance institutions, empowering more than 1.8 million Micro Entrepreneurs nationwide. Since its first branch opened in Camarin, Caloocan in 2004, ASA has grown rapidly while maintaining its commitment to dignity, financial inclusion, and community transformation. ASA is known for providing meaningful career opportunities while enabling employees to positively impact Filipino communities.
Role Description
This is a project-based consultant role under the AI & Data Tech Team of the TSD Group, with work-from-home flexibility and occasional on-site work at the Taguig office.
The AI & Data Engineer will serve as a hybrid AI/ML Engineer + Data Engineer + Applied Data Scientist, responsible for developing AI-driven features, designing data pipelines, building ML-ready datasets, and deploying ML models that power CAMS 2.0 and ASA's digital transformation roadmap.
The role requires strong engineering capabilities, hands-on machine learning experience, applied Data Science skills, and deep familiarity with Google Cloud AI tools; including Vertex AI, BigQuery ML, and Gemini Enterprise for GenAI-driven analysis, automation, and development acceleration
Key Responsibilities
A. AI/ML Engineering
Build end-to-end ML workflows using Vertex AI (training evaluation deployment monitoring).
Develop ML models for: Borrower verification, Credit scoring, Affordability validation, Risk & fraud detection, Forecasting (clients, portfolio, PAR trends), Collections and repayment behavior
Implement MLOps automation (model versioning, CI/CD for ML, model monitoring).
Use BigQuery ML to create fast, scalable models when deep ML is not required.
B. Applied Data Science
Perform exploratory data analysis (EDA) to understand trends and anomalies.
Conduct feature engineering and create AI-ready datasets.
Use statistical and ML techniques (regression, clustering, classification, anomaly detection, time-series forecasting).
Evaluate model performance using metrics such as ROC, AUC, RMSE, accuracy, precision/recall.
Translate field operations needs into measurable AI models.
Provide insights to support Ops, Finance, and Audit decision-making.
C. Data Engineering
Design and maintain scalable data pipelines using Google Cloud: Datastream, Dataflow/Dataproc, Cloud Composer, BigQuery, Cloud SQL
Implement ETL/ELT workflows using Python, SQL, or Cloud-native tools.
Manage data quality, data validation, and lineage.
Create unified data models supporting AI, analytics, and dashboards.
Collaborate with the Data Governance team for accuracy, security, and compliance.
D. Collaboration & Delivery
Work with Product Owners and App Dev to integrate ML models into CAMS 2.0.
Coordinate with Field Ops, Risk, and Finance teams to align AI outputs with business rules.
Support compliance initiatives aligned with audit requirements.
Document ML pipelines, data flows, model decisions, and performance results.
Qualifications
Core AI/ML & Data Science
Strong experience building ML models using: Vertex AI, BigQuery ML, Scikit-learn / TensorFlow / PyTorch (optional but good to have)
Skilled in feature engineering, model training, testing, tuning, and evaluation.
Understanding of statistical modeling, ML concepts, and applied Data Science techniques.
Ability to translate business problems into ML solutions.
Data Engineering
Strong skills in Python and SQL.
Experience with ETL/ELT, data warehousing, and pipeline automation.
Hands-on with BigQuery, Datastream, Cloud Composer, Dataflow/Dataproc.
Knowledge in database design and data modeling.
Tools & Technologies
Google Cloud Platform (GCP)
Vertex AI, BigQuery, Cloud SQL
Python, SQL
Git-based workflows and CI/CD
Looker Studio or similar analytics tools
Soft Skills & Mindset
Strong analytical and problem-solving skills.
Ability to explain AI outputs in layman terms.
Comfortable collaborating with senior leaders
Attention to detail and commitment to data accuracy.
Education
Bachelor's degree in Computer Science, Data Science, IT, Engineering, or related fields.
Master's degree is an advantage.
Experience in microfinance, fintech, or regulated industries is a plus.
