About the position
Translate business problems into data-driven and AI-enabled solutions.
Perform exploratory data analysis to uncover patterns, issues, and opportunities.
Design, build, and maintain data pipelines to support analytics and modelling use cases.
Develop, train, evaluate, and iterate on machine learning and AI models.
Apply appropriate model evaluation techniques and define success metrics.
Support operational data workflows and resolve day-to-day data processing issues when required.
Produce clear dashboards, reports, and visualisations for stakeholders.
Communicate insights, model behaviour, and recommendations to both technical and business audiences.
Collaborate closely with data engineering, AI platform, and observability teams to productionise solutions.
Contribute to best practices around data quality, governance, and responsible use of AI.
Minimum Requirements:
Qualifications/Experience:
An ideal candidate:
Is outcome-driven, not model-driven.
Is comfortable working close to data pipelines and production constraints.
Can explain models and uncertainty to business stakeholders.
Collaborates naturally with platform, AI engineering, and observability teams.
Understands that trust, transparency, and governance are as important as accuracy.
Essential Skills Requirements:
Data analysis, exploration, and feature engineering (EDA).
Strong applied statistics and machine learning foundations.
Python-based data science and ML stack (e.g. pandas, NumPy, scikit-learn, PyTorch / TensorFlow).
Data engineering skills: ETL design, batch and streaming data processing.
Experience with distributed data systems (e.g. Kafka, Spark or equivalent).
SQL and structured / semi-structured data querying.
Experiment design, model evaluation, and validation techniques.
Dashboarding, reporting, and data visualisation for insights and decision support.
Business problem translation and requirements understanding.
Version control and collaborative development (Git).
Advantageous Skills Requirements:
MLOps practices (model packaging, deployment pipelines, monitoring awareness).
Data governance principles (data quality, lineage, ownership, compliance awareness).
Model evaluation, performance tracking, and drift detection concepts.
Cloud-based data and ML environments (Azure / AWS).
Generative AI and LLM-based solution experience (preferred, not essential).
AI agent or advanced prompting familiarity.
Experience collaborating with observability and platform engineering teams.
Domain-specific knowledge aligned to business use cases.
Desired Skills:
- Data engineering skills
- Python
- ML stack
