Senior Data Scientist, Internal Audit & Strategic Projects

About you

CARSOME is seeking a Senior Data Scientist who is passionate about using data to strengthen internal governance. In this role, you will partner with Internal Audit, Risk Management, and Compliance teams to enhance oversight and control through data science, automation, and AI-driven solutions. You will identify and deliver impactful projects that improve risk visibility, streamline compliance efforts, and drive efficiencies across the
governance landscape.

This is a unique opportunity for a data scientist with a strong sense of integrity and curiosity to shape the future of audit and risk management in a tech-driven organisation.

Your Day-to-Day

• Work closely with Internal Audit, Risk, and Compliance teams to understand control processes and identify opportunities for digital transformation through data science.
• Design and lead governance-focused analytics and AI initiatives, such as:
  • Automated identification of control failures via exception analytics (e.g., duplicate payments, late
    approvals, missing documentation);
  • Transaction-level risk scoring to support continuous auditing or targeted sampling during audits.
  • Process mining to visualise actual vs. expected workflows (e.g., procure-to-pay, sales-to-cash) and flag
    deviations;
  • Anomaly detection in financial, procurement, or operational data sets to uncover outliers or red flags;
  • Audit planning support through risk-based analytics to prioritise audit areas using historical incident data
    and key risk indicators (KRIs);
  • Natural Language Processing (NLP) for automated reviews of policy compliance, contract terms, or
    internal reports;
  • Predictive models for identifying high-risk vendors, business units, or employees based on behavioural
    or transactional patterns;
  • Data quality and integrity analytics to identify inconsistencies or missing records in core audit data sets.

• Develop dashboards, control monitoring tools, and machine learning models to provide real-time visibility into governance and compliance status.
• Drive automation of audit and compliance testing procedures, leveraging Artificial Intelligence (AI) and Machine Learning (ML) where applicable to enhance coverage and reduce manual workloads.
• Collaborate with MLOps engineers to deploy models and analytics pipelines with appropriate monitoring, governance, and audit trails.
• Ensure governance of deployed models, including performance tracking, drift detection, and compliance with
  documentation and transparency requirements.
• Partner with Data Engineering and Tech teams to improve the quality and availability of data relevant to audit
  and risk assessments.
• Document methodologies and outcomes with a focus on transparency, reproducibility, and auditability

Your-Know-How

• Strong ability to translate governance-related problems into data science solutions with measurable
  outcomes.
• Minimum of three (3) years of hands-on experience in data science or advanced analytics, preferably with
  exposure to internal audit, compliance, or risk functions.
• Solid knowledge of Python (preferred) and/or R, with proficiency in SQL for data exploration and analysis.
• Practical experience with the full model lifecycle – from ideation and development to deployment and
  monitoring – in high-trust environments.
• Familiarity with MLOps practices, version control, and deployment of models in production settings.
• Understanding of model governance, explainability, and ethical AI principles, especially in the context of
  control or regulatory functions.
• Excellent communication skills, able to explain complex technical concepts to non-technical stakeholders
  and work collaboratively with auditors and compliance professionals.
• Resourceful, self-directed, and motivated to identify gaps and proactively propose data-driven
  improvements.
• Experience working with Google BigQuery, AWS/GCP, and visualisation tools such as Looker, Power BI, or
  similar platforms.
• Background in internal audit, risk management, or compliance, or experience working closely with such
  teams is an added advantage.
• Knowledge of data privacy regulations (e.g., PDPA) and how they apply to data science work.Exposure to audit analytics platforms, continuous auditing tools, or process mining solutions (e.g., Celonis, UIPath Process Mining) is an added advantage.
• Experience building AI tools for documentation analysis or real-time control monitoring is an added advantage.
• Experience in a regulated or high-trust industry (e.g., fintech, automotive) is an added advantage.