Data Analytics Manager

Wirehire; Daerah Khusus Ibukota Jakarta; On-site; IDR 100.000.000 - 200.000.000; Full time

Job summary

A financial technology firm in Jakarta seeks a skilled professional to build predictive models for credit risk and fraud in the financial sector. Applicants should have a Bachelor's degree in a quantitative field and at least 4 years of experience in machine learning for finance. Strong communication and leadership skills are essential for this role.

Qualifications

• 4+ years of experience in building machine learning models in finance.
• Solid understanding of mathematical and statistical concepts.
• Experience in communicating with business stakeholders.

Responsibilities

• Build predictive models for credit risk and fraud.
• Manage the entire lifecycle of model development.
• Engineer predictive features from existing data.

Skills

Building predictive models

Data analytics

Communication skills

Leadership

Feature creation

Education

Bachelor's degree in a quantitative field

Job description

• Build predictive models for credit risk, collections, fraud, and other business needs in the financial industry.
• Manage and own the entire end-to-end lifecycle of building, validating, deploying, and maintaining predictive models.
• Engineer predictive features from existing data to create refined customer profiles.
• Identify external data assets to integrate into the models.
• Conceptualize and design analytic model frameworks to solve business problems.
• Utilize strong communication skills to share insights and lead strategy.
• Ensure multiple stakeholders buy into the vision and execution of the analytics roadmap.

Requirements:

• Bachelor's degree or equivalent experience in a quantitative field (Statistics, Mathematics, Computer Science, Engineering, etc.).
• At least 4 years of hands‑on experience in building, evaluating, and monitoring machine learning models for collection risk or consumer credit scoring.
• At least 2 years of leadership experience.
• Solid understanding of mathematics and statistics.
• Knowledge of machine learning concepts such as Bagging, Boosting, Recommendation Engines, etc.
• Expertise in feature creation on a variety of data types.
• Professional experience in developing machine learning analytics models.
• Understanding of trade‑offs between model performance and business needs.
• Proven ability to formulate data science solutions to business problems.
• Proven ability to communicate with business stakeholders.
• Experience and knowledge in more than one domain (Risk Analytics, Marketing Analytics, Fraud Analytics, etc.) is a plus.