Data Science, Specialist

Get To Know Our GX Bank Team

GX Bank Berhad - the Grab-led Digital Bank - is the FIRST digital bank in Malaysia, approved by BNM to commence operations. We aim to leverage technology and innovation to serve the financial needs of the unserved and underserved individuals, and micro and small medium enterprises.

We are driven by our shared purpose and passion to bring positive transformation to the banking industry, starting with solutions that address the financial struggles of Malaysians and businesses.

Get To Know The Role

• Create and execute the data science approach in a startup atmosphere to support the business goals and objectives with commercial value of work prioritisation and execution, which will deliver actionable insights for business planning and execution.
• Develop and deploy analytical solutions across a variety of business functions, including, but not limited to: customer acquisition, customer retention, product development, pricing decisions, credit risk, fraud identification and many other business needs within the Bank for both Retail and Wholesale Banking customers.
• Manage and own the entire end-to-end MLOps life cycle includes data exploration, training data, feature engineering, model development, validation, scoring, deployment via API and model maintenance.
• Interface with business, risk & operation teams across the Bank to formulate solutions & product changes informed by your findings and business inputs/reality.
• Work independently or in a team to solve complex problem statements.
• Being the analytics technical expert who uses large data sets, creative and strong in applying varieties of machine learning methodologies / algorithms with different data tools in developing the models, running simulations & optimization.
• Being an analytics consultant for the business stakeholders to recommend and deliver both innovative and effective analytics solutions in driving continuous improvements and addressing business questions.
• Engineer predictive features from internal data assets to build refined customer profiles. Identify external data assets to bring into the model mix.
• Validate models on new datasets, based on in-market performance.
• Ensure high quality models and seamless integration, which includes model accuracy, automated quality checks, API latencies, deployment time etc.
• Thrive on sharing knowledge with others and helping collaborators grow to foster a positive and productive work environment.
• Stay current on cutting edge machine learning tools and approaches.
  
  

The Must-Haves

• Relevant experience (At least 2 years of experience) in building and deploying machine learning and predictive model solutions on large amounts of data.
• Advanced degree preferred: Masters degree in Computer Science, Applied Mathematics, Statistics, Machine Learning, or a related quantitative field.
• Extensive hands-on experience in coding and modelling skills in Spark, Python, R, SQL, Presto, Hive proficiency.
• Deep technical and data science expertise, including experience in the following:
• Analytical methods: statistical modelling (e.g., logistic regression, time series, CHAID, PCA), supervised machine learning (e.g., random forests, neural networks), unsupervised learning, design of experiments, segmentation/clustering, text mining, network analysis and graphical modelling, optimization, simulation; and
• Experience building in-production models, including associated scripting, error handling and documentation.
• Understanding of trade-offs between model performance and business needs.
• Strong business acumen, inherent curiosity about data, stakeholder management and project management skills to prioritise & manage multiple priorities in a fast-paced and multidisciplinary environment.
• Highly self-driven, demonstrates critical thinking, team player & fast learner.
• Work experience and knowledge of more than one domain is a plus - Risk Analytics, Marketing Analytics, Telecom analytics, Retail analytics, Fraud analytics etc.