Get to know the Role:

• Create and execute the data science & analytics approach and roadmap in a startup atmosphere to support the business goals and objectives with commercial value of work prioritization and execution, which will deliver actionable insights for business planning and execution.

• Lead, coach and guide the junior team members in developing and deploying analytical solutions, data science models and/or analytics insights for management and business teams across the Digibank's regional footprint to make informed business decisions, 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 Digibank for both retail and wholesale banking customers.

• Uncover and deliver actionable insights, trends and product recommendations to support the business, through dashboards and advanced visualization techniques.

• Conduct both historical and predictive analyses and convert into digestible, readable, and publicly-available insights.

• Design and develop Generative AI solutions to boost productivity and operational efficiency.

• Develop Generative AI / AI solutions to help the data team and other disciplines be more effective in debugging, data summarization and analysis of results.

• Manage and own the entire end-to-end MLOps life cycle includes data exploration, training data, feature engineering, model development, validation, scoring, codes standardization, unit testing, deployment via API and model maintenance.

• Interface with business, risk & operation teams across the countries within the region to formulate solutions & product changes informed by your findings and business inputs/reality.

• Being the analytics technical expert with hands-on experiences 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.

• Ensure high quality models and seamless integration, which includes model accuracy, automated quality checks, API latencies, deployment time etc.

• Write SQL scripts for data transformation.

• Ensure data accuracy and good segmentation of data sets for tactical data mining.

• Taking on the responsibilities as a Technical Project Manager to drive projects, by partnering closely with the broader business, product, engineering and marketing teams to define requirements, design and analyze experiments that drive key product designs and marketing decisions.

• Creating data products which can be readily used by partners and business units across the bank.

• Act as the liaison officer between business units across the countries within the region and Data Engineering team on curating the data to be stored in a database and then used for dashboarding or analytics use cases.

• 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:

• Significant relevant experience (At least 5 years of experience) in end-to-end dashboard development life cycle, building and deploying machine learning and predictive model solutions on large amounts of data.

• Bachelors or Masters degree in Statistics, Analytics, Economics, Mathematics, Engineering, Computer Science, Applied Mathematics, Statistics, Machine Learning, or a related quantitative field.

• Advanced in SQL, must have hands-on experience with Python and Tableau (optional).

• 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, optimisation, simulation

- Experience building in-production models, including associated scripting, error handling and documentation

• Experience in cloud platforms like AWS, Azure, GCP etc

• Strong expertise in Generative AI frameworks and models such as GANs, VAEs, GPT, BERT, Claude, DeepSeek etc.

• Proficient in guiding and mentoring the team to ensure timely delivery of high quality analytics

• Solid experience in reporting to management in delivering analyses and reports

• Experience with designing, running and analysing A/B tests and product experiments with a good understanding of hypothesis testing and the basic principles of Design of Experiment.

• Expertise in data retrieval, data modelling (both logical and physical), data warehouse design.

• Passionate about solving problems – possesses a relentless need for investigation and data exploration.

• Results and detail-oriented, with strong intuitions on how to solve problems creatively and quickly.

• Ability to distill data and articulate an actionable point of view to non-technical audiences and senior stakeholders using presentations, interactive visualizations, etc.

• 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, demonstrate critical thinking, team player & fast learner

• Work experience and knowledge of more than one domain is a plus - Risk Analytics, Marketing Analytics, Retail analytics, Fraud analytics etc.

The Nice-to-Haves:

• Familiar with Airflow and DAGs to automate and schedule data pipeline tasks and workflows

• Good to have experience on tools such as dbt, Presto, Hive, and Data Visualisation platforms like Power BI/D3.js