Key Responsibilities:

- Partner with business and technology leaders to identify high-value opportunities for applying data science and machine learning.

- Lead the design, development, and implementation of machine learning solutions aligned with business priorities.

- Own the end-to-end lifecycle of key data science initiatives, ensuring solutions are scalable, reliable, and suitable for real-world business use.

- Guide the selection of appropriate analytical approaches and evaluate solution effectiveness against business objectives.

- Drive the automation and standardization of analytical processes to improve operational efficiency and consistency.

- Establish practices for monitoring model performance, managing risks, and continuously improving deployed solutions.

- Work with data engineering, technology, and business teams to ensure seamless integration of analytical solutions into enterprise processes.

- Identify opportunities to improve business performance, decision quality, and operational efficiency through advanced analytics.

- Provide technical direction, review solution approaches, and mentor data scientists and other team members.

- Communicate complex analytical findings in a clear, business-oriented manner and influence decisions through data-driven recommendations.

- Promote best practices around data quality, model governance, responsible AI, and solution reliability.

- Contribute to the development of the team's capabilities, delivery standards, and long-term data science roadmap.

Required Skills & Experience:

- 8 - 10 years of experience in data science, machine learning, advanced analytics, or related fields.

- Strong hands-on experience in machine learning and statistical modelling, supported by proficiency in Python and SQL.

- Demonstrated experience leading the delivery of machine learning solutions from business problem definition through production implementation.

- Experience designing scalable analytical solutions and improving the performance and reliability of deployed models.

- Strong understanding of model lifecycle management, automation, monitoring, and governance.

- Proven ability to manage complex initiatives and work with multiple business and technology stakeholders.

- Experience mentoring team members and providing technical guidance.

- Strong communication, stakeholder management, analytical thinking, and decision-making skills.

Preferred Qualifications:

- Experience in banking, financial services, lending, credit risk, fraud detection, or other regulated industries.

- Exposure to cloud-based machine learning platforms and enterprise analytics ecosystems.

- Experience driving data science initiatives across multiple business functions or teams.

- Bachelor's or master's degree in Computer Science, Statistics, Mathematics, Data Science, Engineering, or a related discipline.