Lead Data Scientist

Lead Data Scientist

$140,000-$150,000 + 10% bonus

Overview
A growing organisation is seeking an experienced Lead Data Scientist to drive strategic analytics initiatives across customer experience, operational efficiency, asset performance, and cost optimisation. This highly visible role partners closely with senior business leaders to identify challenges, develop data-driven solutions, and influence decision-making through advanced analytics and predictive modelling.
The ideal candidate combines deep technical expertise with strong business acumen, excels at communicating complex concepts to non-technical audiences, and has a proven track record of leading projects from concept through implementation with minimal supervision.


Key Responsibilities

Project Leadership & Stakeholder Management

• Build trusted relationships with senior stakeholders, including director- and executive-level leaders.
• Independently lead projects from problem definition through deployment and performance monitoring.
• Facilitate intake sessions, define project scope, estimate effort, manage priorities, and communicate progress effectively.
• Mentor and guide junior analysts and data scientists on methodology, project structure, and technical execution.
• Present findings and recommendations to leadership teams, including executive audiences.

Advanced Analytics & Data Science

• Develop and deploy predictive models using machine learning techniques such as gradient boosting, ensemble methods, decision trees, and other supervised and unsupervised learning approaches.
• Design segmentation frameworks using clustering and classification techniques.
• Conduct statistical analysis, model validation, back-testing, and performance measurement to ensure reliability and business value.
• Identify patterns, root causes, and opportunities through large-scale data analysis.
• Develop innovative analytical approaches to improve insight generation and operational effectiveness.
• Support business decision-making through forecasting, scoring models, optimisation techniques, and operational analytics.

Data Management & Process Improvement

• Gather, reconcile, clean, validate, and integrate data from multiple internal and external sources.
• Establish data quality standards and controls to improve accuracy and consistency.
• Design and implement processes that reduce risk, improve efficiency, and create measurable business value.
• Monitor and assess the ongoing performance of implemented analytical solutions.

Executive Communication

• Translate complex analytical findings into actionable business recommendations.
• Develop compelling executive presentations and data-driven narratives that influence organisational strategy and operational improvements.

Required Qualifications

• Bachelor's degree in a quantitative field such as Mathematics, Statistics, Engineering, Economics, Computer Science, or a related discipline.
• 10+ years of experience in data science, advanced analytics, predictive modelling, risk analytics, or a related quantitative function.
• 3+ years of hands-on Python experience building machine learning, forecasting, regression, classification, and segmentation models.
• 3+ years of advanced SQL experience, including optimisation of complex queries across large datasets.
• Strong expertise in statistical methods, probability theory, experimental design, predictive analytics, sampling techniques, and model evaluation.
• Demonstrated experience analysing large structured and unstructured datasets.
• Experience presenting analytical insights to executive audiences and influencing strategic decisions.
• Ability to communicate complex technical concepts to business stakeholders with varying levels of analytical expertise.
• Strong project management, prioritisation, and multitasking skills.
• Proven ability to work independently in a fast-paced, evolving environment.
• Experience sourcing, validating, and integrating data from multiple systems and platforms.

Preferred Qualifications

• Master's degree in a quantitative discipline.
• Experience in risk management, portfolio analytics, financial modelling, or asset performance analysis.
• Experience using consumer, credit, demographic, or third-party external datasets.
• Prior experience developing scoring models, predictive risk models, or decision-support frameworks.
• Experience within highly regulated industries where model governance, validation, and audit requirements are critical.
• Familiarity with model risk management frameworks and third-party model review processes.
• Experience working with cloud-based data and analytics environments, including object storage and large-scale compute platforms.
• Experience with modern data warehouse technologies.