Model jobs in Singapore

Analytics Engineers play a critical role in the Data Science workstream within the Analytics Centre of Excellence (CoE). Analytics Engineers are multidisciplinary team members who design, develop, deploy, and monitor machine learning models that support business growth. Analytics Engineers also undertake general and advanced analysis of business problems using various analytical and statistical methods. They support data analytics and all member of the team grow their skills through their interest in using data to solve business problems.

• Responsible for collecting business requirements from stakeholders and proposing machine learning or analytical solutions to business problems.
• Responsible for clearly documenting and articulating how proposed solutions solve business problems.

• Responsible for setting up the required technical and non-technical prerequisites for modelling.
• Leading models developing, writing, and persisting features from structured and unstructured data using a feature store method.
• Develop train and test pipelines for machine learning models with a high degree of code quality.
• Critically evaluating model performance and model fairness using common frameworks that balance performance vs ethical considerations.
• Writing unit tests, undertaking SIT, and supporting UAT of machine learning solutions to ensure a high degree of reliability.
• Documenting solutions in accordance with required standards.

• Writing and refactoring solutions so they are production ready.
• Preparing the required artifacts for deployment such as pipelines, triggers, orchestrators etc.
• Deploying models via various serving layers including batch and API options.
• Writing good quality documentation and presenting solutions to various approval committees.

• Undertake automated and manual analysis of a model’s performance after production.
• Undertake automated and manual analysis of a models key ethical and fairness metrics after production.
• Critically evaluate the business use of a model postproduction to support a model’s transition through its life cycle (replacement, retirement etc)

• Conduct in-depth studies and trend analyses to support data-driven decision-making.
• Collaborate with stakeholders to identify analytical needs and translate them into technical solutions.
• Deliver actionable insights that influence strategic initiatives and operational improvements.
• Conduct ad hoc analysis of data to better understand drivers of business performance and to support business planning. This includes simple and advanced analysis such as statistical tests.
• Use your passion for data and technology to identify new patterns that help the business make decisions.

• Enable self-service analytics by developing reusable data assets and standardized metrics.
• Maintain documentation and governance standards to ensure data quality and consistency.

• Kindness, openness, and the willingness to make the team and yourself better.
• Creating specification documents, attribute mapping documents, functional specifications.
• Finding innovative solutions to business problems and the ability to not-recommend AI and machine learning as a solution if not appropriate.
• Presenting results and recommendations to non-technical business partners and stakeholders to drive decision-making and actions.

• Extensive experience with machine learning frameworks, including scikit-learn, MLFlow, PyCaret, FairLearn, and TensorFlow.
• Strong analytical thinking and data expertise.
• Proven knowledge of traditional machine learning techniques such as linear/logistic regression, clustering, classification, principal component analysis (PCA), recommendation systems, and anomaly detection. Proven expertise in cloud development, including designing and implementing data processes for data warehouses and production environments. Hands-on experience with cloud platforms such as Azure is essential.
• Proficiency in transforming raw data to make it more available, organized, and easier to analyze. This includes improving and optimizing existing data solutions and applying engineering best practices to provide clean, transformed datasets ready for analysis.
• Ability to quickly learn and apply advanced methods, including deep learning, Natural Language Processing (NLP), and Generative AI.
• Proficient in Python and SQL, with a focus on writing clean, reliable, and maintainable code.
• Familiar with Python-based visualization libraries such as matplotlib and plotly.
• Experience using Git for version control and platforms like GitHub or Bitbucket.
• Exposure to PySpark and Scala is an advantage.
• Knowledge of web frameworks and APIs (e.g., Flask, Django, FastAPI) is a plus.
• Familiarity with reporting tools such as Power BI, Qlik, or Tableau is a plus.

• Master’s degree or PhD or equivalent work experience in Mathematics, Statistics, Computer Science, Business Analytics, Economics, Physics, Engineering, or related discipline.
• Knowledge of life insurance practices is advantageous