Data Scientist
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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
Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Mathematics, Engineering, or a related discipline.
3–5 years of relevant experience in data science, machine learning, advanced analytics, or a similar role.
- 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.