Associate 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.

Skills

Supervised Learning, large datasets, False Positive, Data Pipeline, Risk Modeling, Pipeline Development, Classification, Methods&techniques, Feature Engineering, Anomaly Detection, Statistics, Pattern Recognition, Clustering, transactional data, Accuracy and Precision, Operational Workflow