Data Scientist

Key Responsibilities: 

• Analyse large operational datasets to identify patterns, anomalies, outliers and risk indicators.
• Develop approaches for anomaly detection, clustering, segmentation and risk scoring.
• Build supervised machine-learning models where historical outcomes or labelled cases are available.
• Perform feature engineering using structured and unstructured data.
• Design evaluation approaches to assess model performance, including precision, recall, false positives and false negatives.
• Work with domain experts to determine how analytical outputs should be incorporated into operational workflows.
• Build and maintain data-processing and feature pipelines to support modelling at scale.
• Develop production-ready model or scoring pipelines and integrate them with applications built by the delivery teams.
• Monitor model performance, data quality and data drift after deployment.
• Conduct exploratory analysis to identify new opportunities for workflow redesign and automation.
• Support the use of LLMs and other AI techniques alongside traditional statistical and machine-learning approaches where appropriate.
• Develop reusable analytical approaches and components that can be applied across multiple project engagements.

Requirements & Qualifications

A. Educational Qualification & Experience

• Education: Degree in Data Science, Computer Science, Statistics, Machine Learning, Data Analytics or Information Systems preferred.

Experience:

• Experience working with large, messy operational or transactional datasets.
• Experience in applied data science, including statistics, feature engineering, machine learning and model evaluation.
• Experience with techniques such as anomaly detection, classification, clustering or risk modelling.
• Experience building data pipelines and taking models beyond experimentation into production environments.

B. Technical & Professional Competencies

Essential (Must-Have)

• Strong proficiency in Python and SQL.
• Able to translate a business or operational problem into an analytical problem and determine the appropriate modelling approach.
• Familiarity with software engineering practices including Git, automated testing and APIs.
• Comfortable assessing model trade-offs and explaining them clearly to non-technical stakeholders.

Preferred (Advantageous)

• Familiarity with containers, CI/CD or other DevOps / ModelOps practices would be advantageous.

C. Soft Skills & Communication Attributes

• Able to work independently in an ambiguous, fast-moving environment and collaborate closely with engineers, product teams and domain experts.
• Strong sense of ownership for turning analysis into something that is used in an operational workflow.