Role Overview
We are looking for a talented and experienced Data Scientist to join our programme and help design, develop, and deploy advanced AI and Machine Learning solutions. The ideal candidate will have strong expertise in data science, machine learning model development, and production deployment, with the ability to work collaboratively across teams to solve complex business challenges.
Key Responsibilitie
- sDesign, develop, and implement AI/ML-based solutions to address business and regulatory challenges
- .Collaborate with data scientists, engineers, and business stakeholders to build and deploy production-grade machine learning models
- .Perform data analysis, feature engineering, model training, validation, and optimization
- .Conduct exploratory data analysis (EDA) to identify patterns, anomalies, and emerging risk indicators
- .Develop and maintain classification, ranking, and unsupervised learning models, including outlier detection solutions
- .Deploy and manage machine learning solutions using containerized and cloud-based platforms
- .Troubleshoot, debug, and enhance existing code and analytical solutions
- .Monitor model performance and support continuous improvement through validation and retraining
- .Work closely with cross-functional teams to understand business requirements and translate them into scalable analytical solutions
- .Contribute to best practices in model governance, reproducibility, and version control
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Essential Skills & Experien
- ceStrong programming skills in Python, includin
- g:Pand
- asNum
- PyScikit-lea
- rnProficiency in SQL for querying and managing structured dat
- a.Experience in machine learning model development and validation, includin
- g:Classification mode
- lsUnsupervised learning techniques (e.g., anomaly/outlier detectio
- n)Ranking and scoring mode
- lsExperience deploying machine learning models in production environments using containerized platforms such as Podman, AWS SageMaker, or similar pipeline framework
- s.Strong understanding of Git and version control best practice
- s.Experience with time-series analysis to identify trends and assess risks over tim
- e.Expertise in exploratory data analysis (EDA) and feature engineerin
- g.Strong problem-solving skills and the ability to communicate complex analytical findings to technical and non-technical stakeholder