Data scientist (senior)

Job Description

The Senior Data Scientist will be responsible for translating business problems into data-driven and AI-enabled solutions. The role requires strong expertise in data analysis, machine learning, data engineering, and stakeholder engagement, while working closely with data engineering, AI platform, and observability teams.

Key Responsibilities 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 Requirements Essential Skills Excel, SQL, Power BI, AWS and Quicksight Data analysis, exploration, and feature engineering (EDA) Strong applied statistics and machine‑learning foundations Python-based data science and ML stack (e.g. pandas, Num Py, scikit-learn, Py Torch / Tensor Flow) 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 Business problem translation and requirements understanding Version control and collaborative development (Git) Advantageous Skills 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 AI agent or advanced prompting familiarity Experience collaborating with observability and platform engineering teams Domain-specific knowledge aligned to business use cases