Data Scientist

• Frame business challenges as measurable data science and machine learning problems, defining clear objectives, assumptions and success metrics.
• Develop, train, validate and improve classical machine learning and NLP models using Python and modern data science libraries.
• Build end-to-end modelling pipelines covering data preparation, feature engineering, training, validation, evaluation and production handover.
• Use BigQuery and BigQuery ML for scalable data processing, experimentation, model development and analytical workflows.
• Prototype Generative AI solutions using LLMs, embeddings and prompting, including basic Retrieval-Augmented Generation approaches.
• Design robust experiments and evaluation frameworks for both classical ML and LLM-based solutions.
• Assess model performance, quality, fairness, explainability and business outcomes using appropriate AI/ML metrics.
• Produce model cards, technical documentation and evidence required for governance and responsible AI review.
• Collaborate with ML Engineers, Data Engineers, Product teams and business stakeholders to operationalise solutions effectively.
• Mentor junior team members, review analytical work and contribute reusable standards and knowledge across the Data Science community.

• 10+ years of experience across Data Science, Machine Learning Engineering or related applied AI roles.
• Proven experience translating ambiguous business needs into well-framed analytical or AI problems.
• Hands-on experience developing and evaluating classical machine learning, NLP and LLM-based solutions.
• Experience with experiment design, statistical evaluation, model metrics and clear communication of findings.
• Experience creating model documentation, model cards and governance-ready evidence.

Required experience Mandatory skills

Capability

Required experience

Python

Strong hands-on Python for data preparation, modelling, evaluation and reusable analytical code.

Machine learning

Model development, feature engineering, validation, optimisation and performance measurement.

NLP

Text preparation, classification, information extraction, embeddings and semantic retrieval pipelines.

Generative AI

Working knowledge of LLMs, embeddings, prompting and rapid prototype development.

RAG fundamentals

Understanding of chunking, retrieval, grounding and basic evaluation of RAG-based solutions.

BigQuery ML

Practical experience using BigQuery and BigQuery ML for data processing, model training and evaluation.

Experimentation

Hypothesis-driven experiment design, AI/ML metrics and statistical interpretation.

Model governance

Model cards, explainability, responsible AI documentation and governance support.

Preferred experience

• Mentoring or coaching junior Data Scientists, analysts or ML practitioners.
• Exposure to agentic AI, autonomous workflows or LLM-based systems.
• Experience with Google Cloud Platform and Vertex AI.
• Production collaboration with ML Engineering, CI/CD and model monitoring teams.
• Experience delivering AI solutions within financial services or another regulated environment.Technology landscape Python | SQL | BigQuery | BigQuery ML | Scikit-learn | XGBoost | NLP | LLMs | Embeddings | Prompting | RAG | Model Evaluation | Model Cards