Data Scientist
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Key Responsibilities
- Analyze 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 forward-deployed 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 agency engagements.
Requirements & Qualifications
A. Educational Qualification & Experience
- Education: Degree in Data Science, Computer Science, Statistics, Machine Learning,
- Data analytics or information systems is 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 modeling.
- Experience building data pipelines and taking models beyond experimentation into production environments.
B. Technical & Professional Competencies
- 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.