[What you will be working on]

Main Responsibilities:

1. Project Management

a) Project manage and work closely with vendors and internal stakeholders to deliver on data science related implementations ensuring that deliverables and objectives are met within agreed scope and timelines.

b) Collaborate with cross-functional teams, including data engineers, devops engineers, product managers, and business analysts and business stakeholders, to integrate and deploy models into current analytics platforms and production systems.

2. Application of Advanced Analytics and Data Science to Support Strategic Business Objectives

a) Work with large, complex datasets and solve non-routine analysis problems, applying advanced analytical methods as required (e.g., multi-objective optimisation, bayesian optimsation and inference, etc..) to address business use cases and aid in decision making.

b) Prepare, process, cleanse and verify the integrity of data collected for analysis. Apply data mining techniques, perform statistical analysis and build prototype analysis pipelines iteratively to provide insights at scale.

c) Apply advanced machine learning techniques and models (e.g., LLMs, Autoencoders, GANs) to create machine learning-based tools or processes (e.g., recommendation engine(s), synthetic data generation, performance tracking through A/B testing, forecasting and predictive capabilities) to improve the current analytics capabilities.

d) Support and work closely with operational and strategic planning teams through the development of analytical capabilities and tools so that they are able to translate data into actionable insights.

3. Data Collection and Management

a) Collaborate with current team to review the existing data collection methods and make improvements to the current data collection strategy.

b) Enhance data collection procedures to identify and include suitable data for building analytic models relevant for industry transformation.

c) Analyse and assess the effectiveness and accuracy of new data sources (e.g., datasets received from stakeholders, and annotation/ labelling of new training inputs).

d) Work with data and agency partners to assemble large, complex datasets that meet functional and non-functional business requirements.

e) Provide inputs to the design and development of an integrated data model to allow analysis across multiple structured and unstructured datasets.

[What we are looking for]

• Degree from a recognised university in a quantitative discipline: Data Science, Applied Mathematics & Statistics or Computer Science.

• At least 3-5 years of relevant experience working in developing ML / AI mathematical models and successfully deploying at least 1 medium to large scale ML system.

• Proven track record in managing internal and external stakeholders and delivering on objectives according to project timelines.

• Good presentation and communication skills with ability to express complex ideas, data / concepts and outcomes of analysis clearly to business audiences.

• Strong analytical skills with a good eye for detail and possess an aptitude/experience in solving analytical problems using quantitative approaches.

• Ability to integrate and synthesise research and data across multiple sources to derive meaningful conclusions.

• Possess extensive working experience solving analytical problems, including skills in statistical data analysis such as linear models, regression, multivariate analysis, stochastic models and sampling methods.

• Experience in machine learning techniques (e.g., k-NN, Bayesian Networks, SVM, Decision Forests etc.), machine learning frameworks (e.g., TensorFlow, PyTorch) and numerical methods (e.g., multi-objective optimisation, bayesian optimsation etc.)

• Experience in using Qliksense and AWS services (e.g., SageMaker, Athena, RDS, ECR, ECS, EMR, Lambda, Redis) will be advantageous.

• Experience with DevOps and deploying models through MLOps will be advantageous.

• Experience working with structured and unstructured datasets is essential.

• Proficient in statistical programming tools (e.g., R, Python), and database languages (e.g., SQL - DQL, DML, DDL)

• Good command of written and spoken English

• Strong planning, organisational, and time management skills