Data Analytics Manager

Asiacruit BPO, Inc; Jakarta Barat; Hybrid; IDR 1.000.333.000 - 1.333.778.000; Full time

Job summary

A prominent software house in Jakarta is seeking a Data Analytics Manager to lead initiatives in predictive modeling for credit risk and fraud detection. The role requires strong analytical skills, team leadership experience, and expertise in machine learning techniques. As part of a collaborative environment, you will have the opportunity for professional growth and work with a talented team. This position offers a hybrid work model and competitive compensation.

Benefits

Competitive compensation package

Performance-based incentives

Opportunities for professional growth

Flexible work-life balance

Qualifications

• 4+ years of experience with machine learning models in financial products.
• 2+ years of leadership or team management experience.
• Strong foundation in mathematics and statistics.

Responsibilities

• Build predictive models for credit risk and fraud detection.
• Manage the lifecycle of model development from design to deployment.
• Lead data teams to deliver impactful analytical outcomes.

Skills

Machine learning model development

Statistical analysis

Feature engineering

Team leadership

Stakeholder communication

Education

Bachelor’s degree in Statistics, Mathematics, Computer Science, or Engineering

Job description

Work Setup: Hybrid
Location: West of Jakarta
Company Industry: Software house / IT Agency
Employment Type: 6-12 month contract, then permanent

About Asiacruit
Asiacruit is your trusted partner for Employer of Record (EOR) and compliant workforce management solutions. We help businesses scale with global professionals, seamless integration, and cost efficiency.

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

Why Join Asiacruit

Hybrid work setup offering flexibility and work‑life balance.

Work with a diverse, talented team in a collaborative environment.

Opportunities for professional growth and exposure to global projects.

Competitive compensation package and performance-based incentives.

A culture that values innovation, teamwork, and continuous learning.