Data Scientist

Role Overview

We are looking for a Data Scientist to support across 4-5 product squads working with

government agencies to redesign workflows and build AI-enabled solutions.

You will work on problems involving large volumes of operational data. Your role is to

help teams move beyond rules-based automation by identifying patterns in data,

detecting anomalous or higher-risk cases, and building models that help officers

prioritise their attention.

This is an applied role. You should be comfortable moving from exploratory analysis and

modelling to building data pipelines and putting models into production.

Key Responsibilities⚠ REQUIRED

• 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 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 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 nontechnical 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, Machine Learning, large datasets, Risk Modeling, Information Systems, Classification, Model Evaluation, Teaching Qualification, Computer Science, Feature Engineering, Anomaly Detection, Lead Scoring, CASE, Statistics, Pattern Recognition, Clustering