Data Scientist - Risk Analytics & Modelling

Indodana; Jakarta Pusat; On-site; IDR 180,000,000 - 300,000,000; Full time

Job summary

Indodana is seeking a data analyst to monitor portfolio health and perform deep-dive analyses on financial datasets. You will work with business, product, and engineering teams to identify risk drivers and translate findings into credit decisions.

You will be embedded in a fast-paced data science team with access to robust data infrastructure, exploring anomalies, building SQL-based features, and supporting improvements to the credit framework while balancing risk and growth opportunities.

Qualifications

• Bachelor's degree in an analytical or quantitative field.
• Proficient in Python, SQL, and MS Excel.
• Strong communication and teamwork abilities.
• Excellent problem-solving and quick learner.
• Willingness to own projects and work hands-on with data.
• Experience with large datasets and identifying anomalies.

Responsibilities

• Monitor portfolio risk metrics regularly and flag anomalies.
• Conduct root-cause analysis for deteriorating risk indicators.
• Translate findings into actionable credit recommendations.
• Build and maintain SQL-based analyses and dashboards.
• Support credit scoring improvements with model evaluations.

Skills

Python

SQL

MS Excel

Communication

Teamwork

Problem Solving

Data Exploration

Credit Scoring

Education

Bachelor's degree in analytical/quantitative field

Tools

SQL

Excel

Job description

Job Description

We're looking for candidates with strong analytical skills and hands‑on experience working with financial datasets. You will be responsible for monitoring portfolio health, performing deep‑dive exploration and analysis to identify risk drivers and improvements in our existing framework, and translating findings into actionable credit decisions. You will partner with business, product, and engineering teams to solve some of the most challenging problems in lending while also continuously improving portfolio quality by balancing risk control with growth opportunities. You will be embedded in a fast‑paced environment, working within a strong team of data scientists with access to a robust data infrastructure.

Job Description

We're looking for candidates with strong analytical skills and hands‑on experience working with financial datasets. You will be responsible for monitoring portfolio health, performing deep‑dive exploration and analysis to identify risk drivers and improvements in our existing framework, and translating findings into actionable credit decisions. You will partner with business, product, and engineering teams to solve some of the most challenging problems in lending while also continuously improving portfolio quality by balancing risk control with growth opportunities. You will be embedded in a fast‑paced environment, working within a strong team of data scientists with access to a robust data infrastructure.

Responsibilities

• Monitor portfolio risk metrics on a regular basis and proactively flag anomalies or emerging trends
• Perform root cause analysis when risk indicators deteriorate to pinpoint what's driving the change, such as identifying suspicious behavioral patterns
• Translate findings into concrete recommendations and implement adjustments as needed
• Build and maintain SQL‑based features, analyses, and monitoring dashboards to support ongoing risk surveillance
• Perform occasional model evaluation to support credit scoring improvements

Qualifications

• Fresh Graduate/Bachelor's degree in an analytical or quantitative discipline (e.g. math, statistics, engineering, computer science), however other disciplines will be considered
• Experienced in using statistical computer languages such as Python, SQL, and MS Excel
• Have good communication skills and able to work together in a team
• Excellent problem‑solving skills and have the drive to learn and master new technologies and techniques
• Willingness to learn new skills independently and have a strong sense of project ownership
• Not afraid to get your hands dirty exploring data, investigating anomalies, and building SQL‑based analyses
• Comfortable working with large tabular datasets to detect trends and anomalies, and communicating findings as actionable recommendations.
• Exposure to credit scoring modelling concepts is a plus.