Data Scientist & Automation Lead – Vulnerabilities remediation Governance

Overview

In this role you will drive high-impact vulnerability remediation governance by applying advanced analytics and AI to support compliance operations globally. You will build scalable data products and decision-support software, collaborating with compliance, business, and technology teams to turn complex problems into data-driven solutions. You’ll lead end-to-end delivery of production‑grade data science products, contribute to strategy, and mentor junior team members. This is an opportunity to shape how AI and automation uplift risk management and operational efficiency at scale.

Pay / Benefits

• competitive compensation package
• health and wellness programs
• paid volunteer days
• flexible work‑life support
• inclusive development opportunities
• employee networks

Responsibilities

• Acquire data for vulnerability remediation and create usable datasets for modelling; update data collection practices and ensure data integrity with stakeholders
• Evaluate and improve models; establish best practices for production models and conduct thorough method reviews
• Maintain industry knowledge to identify strategy opportunities and contribute to thought leadership; write extensible, debuggable code
• Define business and solution goals; collaborate with teams to identify opportunities and align with customer needs
• Act as technical owner of one or more production‑grade DS product codebases
• Lead end‑to‑end delivery of decision‑support software across the product lifecycle
• Identify opportunities for data‑driven optimisation by understanding complex operational problems
• Prototype, build and deploy industrialised ML and optimisation models in Python
• Design robust data ingestion, cleaning and processing pipelines; implement CI/CD and orchestration
• Ensure software quality with logging, error handling and automated testing
• Harden algorithms against operational and data edge cases; quantify product adoption and value
• Engage stakeholders to gather requirements and feedback; contribute to roadmap discussions
• Support integration of decision‑support products into business processes
• Communicate modelling approaches and results clearly to technical and business audiences
• Mentor junior team members; contribute to agile practices including Git, code reviews and delivery predictability
• Demonstrate expertise in DS, ML, and analytics to deliver predictive and insight‑driven solutions
• Partner with governance, compliance, and technology teams to ensure responsible AI adoption

Key requirements

• 4-6 years of production ML or optimization software experience
• Experience delivering analytics products from concept to deployment
• Strong Python, SQL and data engineering capabilities
• Production‑quality DS software development experience
• Cloud platform experience and modern ML tooling
• Excellent communication skills across technical and non‑technical audiences
• Customer-facing experience and ability to translate business needs into technical solutions
• Experience in process mining, workflow optimisation, and intelligent automation
• Background in solution architecture, design thinking and scalable application development
• Ability to work in ambiguous environments and shape direction with stakeholders

• Communication
• Problem solving
• Detail-oriented
• Python
• SQL
• Data Engineering

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