Data Scientist
4 days ago
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, verify, analyze operational data, transaction data, marketing data, and external data to develop hypotheses and build potential quick win use cases or business improvement recommendations.
- Collaborate with data engineering and IT teams to identify available data for developing the business DataMart, build the models, train the models, validate, test them, and fine-tune on real-world use cases.
- Present models and articulate findings clearly and effectively to line of business, risk team, and technical team during model development.
- Minimum Bachelor's degree in Mathematics, Computer Science, Statistics, Engineering, or a related major.
- Minimum 4 years of experience as a Data Scientist in Banking, Financial Services, or FinTech Company.
- Proficient in scripting and strong in programming languages such as R, Java, and Python, with experience applying Machine Learning frameworks and libraries (TensorFlow, Keras, Spark MLlib, Sklearn, pandas, etc.).
- Familiar or has basic knowledge in data processing with statistical tools (SAS, SPSS, KNIME, Matlab, etc.).
- Intermediate to advanced knowledge in data science methodologies, including but not limited to classical regression, neural networks, association rules, sequence analysis, classification, cluster analysis, gradient boost, and text mining. Strong 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, and A/B experimentation & rollout (production).
- Passionate about data technologies (Big Data, ETL, visualization, AI, ML, Deep Learning, etc.) and detail-oriented with a focus on numbers and data quality.
Mid-Senior level
Employment TypeFull-time
Job FunctionInformation Technology
IndustriesBanking, Financial Services, and Technology, Information and Internet
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