Advanced Analytics Specialist
Gravitas Recruitment Group
Hong Kong
Full time
Permanent
On-site
Competitive
About the job
Role Summary Support the development, maintenance and operationalisation of machine learning and advanced analytics solutions, with a primary focus on maintaining and enhancing existing analytical models, feature engineering pipelines and Python/PySpark-based codebases. Work closely with the Advanced Analytics team to improve code quality, end-to-end model reliability, maintainability and production readiness, solving complex business problems through advanced analytics, machine learning and AI. Job Description Model & Feature Maintenance
- Maintain and enhance existing machine learning models, feature engineering logic and analytical pipelines using Python, PySpark and SQL.
- Refactor existing code to improve readability, modularity, reusability, performance and maintainability.
- Support model retraining, recalibration, feature updates and validation, ensuring consistent implementation across model training and scoring processes.
- Support the development, validation and enhancement of predictive models and machine learning solutions, such as propensity, churn, customer lifetime value, recommendation and forecasting models.
- Perform model evaluation, calibration, back-testing, feature selection and hyperparameter tuning.
- Translate defined business requirements into practical and maintainable analytical deliverables.
- Support established machine learning lifecycle processes, including testing, deployment, monitoring and retraining.
- Convert prototype code into reusable, maintainable and production-ready Python, PySpark and SQL components.
- Maintain model pipelines, experiment records, model artifacts, dependencies and deployment configurations; support automated testing, version control, job scheduling and release management.
- Collaborate with data engineering and technology teams, adhering to established MLOps, model governance, security and documentation standards.
- Monitor model performance, data quality, feature availability, prediction distributions and model stability.
- Investigate model deterioration, data and feature drift, scoring anomalies, code defects and pipeline failures, and support production issue resolution.
- Support selected AI use cases involving Generative AI, LLMs, RAG or AI agents through prototyping, testing and evaluation.
- Bachelor’s degree in Data Science, Statistics, Mathematics, Computer Science, Engineering, Actuarial Science or another related quantitative discipline.
- Minimum 5 years of relevant experience in data science, machine learning, advanced analytics, data and AI engineering, or a related quantitative field.
- Proven experience deploying advanced analytics or machine learning solutions in a business environment.
- Experience collaborating with analytics, business, IT and governance teams.
- Strong hands-on programming skills in Python, PySpark and SQL, including debugging, refactoring and code optimisation.
- Practical experience developing, maintaining, testing and enhancing machine learning models and analytical pipelines.
- Practical experience with machine learning libraries such as Scikit-learn, XGBoost, LightGBM or equivalent.
- Practical experience with supervised machine learning, predictive modelling and statistical analysis.
- Practical experience in feature engineering, data preprocessing, model validation and performance analysis.
- Familiarity with MLOps practices, including deployment pipelines, version control, automated testing, CI/CD and production support.
- Exposure to Generative AI, LLMs, vector databases, RAG architectures or agentic workflows would be an advantage.
Gravitas Recruitment Group