Key Requirement & Responsibilities
- Machine Learning Expertise: Apply a wide range of machine learning algorithms for tasks such as demand forecasting, price elasticity/sensitivity modeling, customer segmentation, churn prediction, and next base offer.
- Personalized Pricing Recommendation: Utilizing customer data to offer optimal pricing tailored to individual needs and behaviors.
- Graph Analytics Development: Spearhead initiatives utilizing graph databases and graph analytics to uncover hidden relationships and patterns within customer networks, transaction data, and product interactions.
- Data-Driven Insights & Communication: Translate complex analytical findings into clear, actionable business recommendations for stakeholders.
- Collaboration & Mentorship: Work collaboratively with cross-functional teams (product, finance, IT) and mentor junior data scientists within the team, fostering a culture of continuous learning and analytical excellence.
- Model Implementation & Monitoring: Ensure robust deployment, monitoring, and ongoing optimization of analytical models in a production environment.
- Monitor and analyze competitive pricing trends to maintain a competitive edge
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Required Skills and Experience:
- Advance analytics & Machine learning skills: Proficiency in statistical modeling, predictive analytics, and various machine learning techniques (e.g., regression, classification, clustering, time series analysis, deep learning).
- Technical expertise: Strong command of programming languages (SQL, Python and/or R) and data visualization tools (Looker/Power BI/Tableau).
- Graph analytics: Familiarity with graph databases (Neo4j / GraphX) and graph algorithms.
- Domain knowledge: Basic understanding of banking products, financial markets, and basic finance.
- Problem-solving skills: Ability to identify and solve complex business problems through data-driven approaches.
- Communication skills: Both written and verbal, English and Bahasa Indonesia
Preferred Qualifications:
- Advanced degree in statistics, mathematics, computer science, or economics.
- Experience in a financial institution.
- Certifications in data science or machine learning.
