As a Data Science Lead at KBZ Pay (Fintech) and KBZ Bank, you will play a crucial role in leveraging data-driven insights to enhance business strategies and decision-making processes. This role offers you the opportunity to lead a team of data scientists, machine learning engineers and collaborate with cross-functional teams to drive innovation and achieve business objectives. The role involves working with large datasets, implementing advanced predictive analytics, and contributing to the development of data-driven solutions. At KBZ Pay and KBZ Bank, we are committed to revolutionizing the fintech landscape through data-driven innovation. Your expertise will be instrumental in shaping this transformation.

1. Technical Leadership:

Foster and mentor: Cultivate a collaborative and high-performing data science team through effective coaching, development programs, and knowledge sharing.

Strategize and guide: Define the technical roadmap for data science initiatives, aligning with business goals and ensuring efficient resource allocation.

Champion best practices: Implement and enforce data quality standards, model governance principles, and secure development practices.

Drive continuous improvement: Evaluate team performance, identify opportunities for development, and implement process improvements for greater efficiency and impact.

2. Business Impact:

Translate data into insights: Collaborate with cross-functional stakeholders to analyse business needs, translate data into actionable insights, and drive data-informed decision-making.

Develop and deploy solutions: Design, build, and implement data-driven solutions that address key business challenges and optimize customer experiences.

Track and measure success: Monitor the performance of data science initiatives, evaluate their impact on business objectives, and optimize solutions for continuous improvement.

Communicate value: Effectively communicate the business value of data science to a range of stakeholders, both technical and non-technical.

3. Data Architecture and Infrastructure:

Optimize data pipelines: Collaborate with engineering teams to design and implement efficient data pipelines for data ingestion, transformation, and analysis.

Maintain and evolve data infrastructure: Manage and optimize the AWS-based data infrastructure, ensuring scalability, security, and compliance with best practices.

Champion new technologies: Evaluate and advocate for the adoption of new data technologies and tools that can enhance the team's capabilities and impact.

4. Continuous Learning and Innovation:

Stay ahead of the curve: Actively pursue learning opportunities to stay current with the latest advancements in data science, machine learning, and fintech trends.

Foster a culture of innovation: Encourage creativity and experimentation within the team, promoting the exploration of new data-driven solutions and approaches.

Share knowledge and insights: Organize knowledge-sharing sessions and actively contribute to the dissemination of data science best practices within the organization.

• 7 years of professional experience in related fields and industries.
• 3 years of hands-on experience in data science and machine learning profiles.
• A Master's degree or Ph.D. in a relevant field (e.g., Data Science, Computer Science, Statistics) is strongly preferred. we will also consider candidates with a Bachelor's degree in a relevant field and 3 years of hands-on experience in data science.
• We are primarily seeking a candidate with strong analytical skills, proficiency in machine learning techniques, and experience in developing production-ready data-driven solutions with proven experience leading a data science team in a fintech or banking environment.

• Strong proficiency in using AWS services for data storage, processing, and analysis.

• Expertise in statistical analysis, machine learning, and predictive modelling.

• Proficient in programming languages such as Python, R, or Scala.
• Excellent communication skills with the ability to convey complex technical concepts to non-technical stakeholders