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3 дня назад

Data Scientist - Risk Analytics & Modelling (Fintech)

Формат работы
onsite
Тип работы
fulltime
Грейд
junior
Английский
b2
Страна
Indonesia
Вакансия из списка Hirify.GlobalВакансия из Hirify Global, списка международных tech-компаний
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Описание вакансии

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TL;DR
Data Scientist - Risk Analytics & Modelling (Fintech): Monitoring lending portfolio health and analyzing financial datasets with an accent on risk drivers, anomaly detection, and SQL-based surveillance. Focus on performing root cause analysis, translating findings into credit decisions, and evaluating models to improve credit scoring.

Location: Jakarta, Indonesia

Company

hirify.global operates in the lending and financial technology space, using data-driven products and analytics to support credit decisions and portfolio quality.

What you will do

  • Monitor portfolio risk metrics and proactively identify anomalies and emerging trends.
  • Perform root cause analysis to identify risk drivers and suspicious behavioral patterns.
  • Translate analytical findings into actionable credit recommendations and implement adjustments.
  • Build and maintain SQL-based features, analyses, and monitoring dashboards.
  • Evaluate models to support improvements in credit scoring.
  • Collaborate with business, product, engineering, and data science teams on lending problems.

Requirements

  • Fresh graduate or bachelor's degree in an analytical or quantitative discipline such as mathematics, statistics, engineering, or computer science.
  • Hands-on experience with Python, SQL, and Microsoft Excel.
  • Comfort working with large tabular financial datasets to detect trends and anomalies.
  • Strong communication, problem-solving, independent learning, and project ownership skills.
  • Ability to communicate analytical findings as actionable recommendations and work effectively in a team.

Nice to have

  • Exposure to credit scoring modelling concepts.

Culture & Benefits

  • Full-time role in a fast-paced working environment.
  • Work within a strong data science team with access to robust data infrastructure.
  • Opportunity to learn new technologies and techniques while improving lending portfolio quality.

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