Drive leadership in Data Science team in developing and deploy predictive models for customer segmentation, churn prediction, and personalization services.
Experiences in Big Data technologies to improve scalability, real-time analytics, and data-driven decision-making.
Explore and implement AI technologies including Gen AI, LLM, and other advanced AI applications to improve Our operational processes and enhance customer experience.
Design and implement ML algorithms and Auto-ML for recommendation systems, personalized marketing, and other use cases.
Collaborate with Data Engineering to ensure scalable data pipelines, robust data architecture, and efficient data lake/warehouse solutions.
Perform exploratory data analysis and generate actionable insights for business stakeholders.
Lead initiatives on advanced analytics, including NLP, conversational AI, and intelligent automation.
Ensure compliance with data governance, security, and regulatory requirements.
Able to review model flow, data flow and architecture, and understand the impact of changes to data technology that will be implemented.
Mentor junior data scientists and contribute to building best practices in model development and deployment.
Partner with IT and business teams to integrate models into production systems and monitor performance.
Requirements
Job Requirements
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, or related field.
Minimum 7+ years of experience in data science roles, preferably in banking, fintech, or digital platforms.
Proven track record of delivering machine learning and AI models in production environments.
Strong proficiency in Python or R, and experience with ML frameworks (TensorFlow, PyTorch, Scikit-learn).
Hands-on experience with Gen AI, LLMs, and related frameworks (e.g., Hugging Face, LangChain).
Expertise in Big Data technologies such as Apache Spark, Hadoop, Kafka, and distributed computing.
Strong SQL skills and familiarity with data lake/warehouse architectures.
Experience with cloud platforms and containerization.
Solid understanding of data modeling, feature engineering, and model evaluation techniques.
Knowledge of MLOps practices and tools for CI/CD in ML workflows.
Familiarity with digital banking products, customer lifecycle, and risk management.
Understanding of regulatory compliance in Indonesian banking.
