Data Scientist  

A Data Scientist is a specialist in doing deep-dive analytics, with advanced-level proficiency in data analytics by applying data science methodology, creating model algorithms (Mathematics or Statistical Model), data mining technique and storytelling delivery skills.

Key Responsibilities

• You will work closely with Tribe Leaders, Product Managers & Data Team in identifying potential data science solutions to improve Lending and funding portfolio‘s performance
• Consolidate, verifying, analyzing, from operational data, transaction data, marketing data, and external data to develop hypotheses and building potential quick win use case or business improvement recommendation
• Hand in hand with data engineering and IT team identifying potential available data, for developing the business DataMart, build the models, train the model, validate, test it and fine tuning on real world use case
• In charge and comfortable in model presentation, articulating, communicating in concise, effective and clear communication during the model development towards line of business, risk team, and technical team

• Minimum Bachelor's degree in Mathematics, Computer Science, Statistics, Engineering and related major is preferred
• Minimum 2 years experience as Data Scientist at Banking, Financial Services / FinTech Company
• Comfortable in scripting and strong in some of the following programming languages: R, Java, Python with experience to apply Machine Learning frameworks and libraries (TensorFlow, Keras, Spark MLlib, Sklearn, pandas, etc)
• Familiar or has basic knowledge in one of data processing with statistic tools (SAS, SPSS, KNIME, Matlab, etc)
• Intermediate to advanced knowledge in some of data science methodologies either classic or black box including but not limited to classical regression, neural network, association rules, sequence analysis, classification, cluster analysis, gradient boost, text mining, etc.
• Solid analytical and problem-solving skills to interpret, derive and create data-driven insights
• Actively contribute in all aspects of ML model development: data wrangling, feature engineering, model selection / architecture, training, offline evaluation, plans A/B experimentation & roll out (production)
• Passionate and/or practicing in data technologies (Big Data, ETL, visualization, AI, ML, Deep Learning, etc) and care with details, numbers, and data quality.