Deskripsi pekerjaan Data Analytic Manager Asiacruit BPO Inc
About Asiacruit
At Asiacruit, we connect top talent with forward-thinking organizations across industries. Our mission is to help businesses grow through smart, strategic, and people-focused solutions. If you’re passionate about solving complex technical problems and driving innovation, we’d love to have you on our team.
Position Overview
We're assisting one of our clients in software house / IT Agency who is seeking a Data Analytics Manager to lead advanced analytics initiatives and drive data-powered decision-making. This role will focus on building predictive models related to credit risk, collections, fraud, and other business functions while managing the full lifecycle of model development, deployment, and optimization. The ideal candidate will be highly analytical, business-oriented, and capable of leading stakeholders through data-driven strategies.
Key Responsibilities
- Build predictive models for credit risk, collections, fraud detection, and business optimization
- Manage the full lifecycle of model development—from design and validation to deployment and maintenance
- Engineer predictive features and enhance customer profiling using diverse datasets
- Identify and integrate external data sources to enrich modeling performance
- Design analytical model frameworks aligned with strategic business needs
- Translate complex analytical insights into business-friendly recommendations
- Communicate findings effectively to stakeholders and align execution with analytics roadmap
- Lead and guide data teams to deliver high-impact analytical outcomes
Qualifications
- Bachelor’s degree in Statistics, Mathematics, Computer Science, Engineering, or other quantitative fields
- Minimum 4 years of hands-on experience building and monitoring machine learning models in financial products (credit scoring, collection risk, etc.)
- At least 2 years of leadership or team management experience
- Strong foundation in mathematics and statistics
- Knowledge of machine learning techniques such as Bagging, Boosting, Recommendation Engines, and related concepts
- Expertise in feature engineering across varied data types
- Proven experience developing ML analytics models in real-world environments
- Strong understanding of trade-offs between model performance and business priorities
- Ability to translate business needs into data science solutions
- Strong communication skills and stakeholder engagement capability
- Experience across multiple domains (Risk Analytics, Marketing Analytics, Fraud Analytics) is a plus
