GPAA Data Scientist / MLOps / Data Engineer

Requirements 'must have':

Data Science & Machine Learning:

- Substantial hands-on experience applying statistical modeling and machine learning to solve complex business problems.

- Expertise in Exploratory and Confirmatory Data Analysis, Statistics, Probability, and Optimization.

- Proficiency in data analysis, preprocessing, and preparation.

- Experience with Time Series Forecasting (applied industrial experience is a plus), Anomaly Detection, NLP, GenAI, Deep Learning.

- Advanced knowledge of Python, experience with Kedro.

Cloud & MLOps/DevOps:

- Experience with Cloud infrastructure & services (AWS).

- Solid understanding of MLOps and DevOps key principles.

- Container expertise, including Dockerisation and cloud-native Kubernetes services (e.g., Amazon EKS, GKE, AKS).

- Proficiency in Continuous Integration / Continuous Delivery / Automation (e.g., GitHub, GitLab).

- Infrastructure Automation: Configuration Management and Infrastructure as Code using Terraform (e.g., AWS CloudFormation, AWS CDK).

Data Engineering & Architecture:

- Experience in Data Warehousing design and development (e.g., Snowflake).

- Strong SQL development skills.

- Ability to design end-to-end data pipelines.

- Expertise in Solution Architecture design.

Visualization:

- Familiarity with visualization tools (e.g., Streamlit, Posit, Tableau).

- Full-stack knowledge and troubleshooting experience.

Requirements 'nice to have':

Understanding of finance processes is a plus.