Job Vacancies at I&M Bank

Job Purpose:

• The role holder is responsible for providing independent assurance on the governance, risk management, and control (GRC) processes related to information technology, including general and application controls, data integrity, confidentiality, availability, and system security.

Key Responsibilities:

• Participate and contribute to assessment of risk for target audit areas within the IS space as guided by the Manager, IS Audit.
• Formulate engagement audit plans, prepare planning documents and ensure alignment of objectives, scope, methodology, and IT risk considerations in collaboration with the Manager, Information Systems Audit.
• Develop engagement audit tests.
• Execute audit procedures in alignment with IS audit objectives to ensure effectiveness and efficiency in assessing the design and operating effectiveness of controls.
• Document complete and accurate audit evidence and working papers to support audit conclusions and recommendations.
• Identify and document control weaknesses and associated risks for inclusion in audit reports, ensuring appropriate escalation to relevant stakeholders.
• Lead assigned IS audit engagements, ensuring timely execution of audit procedures, monitoring progress against the approved plan, and identifying opportunities to enhance processes and strengthen controls.
• Continuously evaluate audit objectives, test procedures, and risk coverage to ensure comprehensive coverage of the technology environment.
• Engage with audit clients to validate root causes and ensure proposed management actions are appropriate and actionable.
• Support special audits involving Information systems as assigned.
• Review the governance, risk management, and control environment across IT development projects, DevOps practices, and software engineering workflows, including SDLC processes, CI/CD pipelines, environment management, and agile delivery models. 

Academic Qualifications:

• Bachelor’s in information systems / computer science / IT / Business-related field, or equivalent.

Professional Qualifications:

• Certified Information Systems Auditor (CISA) – required.
• One (1) of: CISM/ CRISC/ CGEIT /CIA /ISO/IEC 27001 Lead Auditor or Implementer/ CISSP/ CIAQA / CCNA/ CPA /CEH / CHFI (added advantage). 

Membership Affiliations:

• Information Systems Audit and Control Association (ISACA).

Work Experience Required:

• Over five (5) years in a similar role in a similar-sized organization.

Competencies:

• Planning & Organizational skills.
• Advanced analytical skills and attention to detail.
• Strong oral and written communication skills.
• Effective interpersonal skills to manage stakeholders at all levels. 
• High standards of ethics and integrity.
• Sound judgment for risk-based audit planning, scoping, and prioritization.
• Strong commercial awareness to align audit outcomes with business objectives.
• Banking Knowledge.
• Comprehensive risk knowledge across IT and operational domains.
• Experience in reviewing DevOps and IT engineering processes, including CI/CD pipelines and agile practices. 

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Job Purpose:

• The Data Scientist will develop and implement data science models and analytical solutions that address business challenges and uncover growth opportunities.
• The Data Scientist applies statistical techniques, machine learning algorithms, and data science methods to derive insights and support evidence-based decisions.

Key Responsibilities:
Strategic: 

• Stay abreast of new ML/AI methods and propose applicable solutions.
• Align model development efforts with broader team priorities and ethics guidelines.
• Provide input into project scoping and business value estimation.

Initiatives:

• Design, develop, and deploy machine learning models.
• Clean, prepare, and engineer features from structured and unstructured data.
• Collaborate with stakeholders to ensure models address real business problems.
• Present insights and model results in understandable formats

Operational:

• Write production-ready code and maintain model pipelines.
• Conduct peer reviews and contribute to code repositories.
• Document assumptions, methodologies, and model limitations.
• Monitor model performance and recalibrate as needed.
• Contribute to internal knowledge sharing and continuous learning efforts.

Key Duties:
Problem Scoping: 

• Work with business teams to define problems.
• Frame use cases into model-ready formats.
• Perform initial feasibility assessments.    

Data Preparation:    

• Extract, clean, and prepare data.
• Conduct exploratory data analysis.
• Engineer relevant features.

Model Development:

• Build machine learning/statistical models.
• Train and tune models using appropriate metrics.
• Evaluate performance and robustness.    

Insight Communication:

• Visualize and interpret results.
• Translate findings into business language.
• Support adoption and stakeholder understanding.    

Deployment and Monitoring:

• Package models for deployment (API, batch, etc.).
• Monitor performance and drift.
• Maintain logs and feedback loops.    

Collaboration and Learning:    

• Participate in peer reviews and team learning.
• Stay updated on new tools/techniques.
• Contribute to knowledge sharing.

Academic Qualifications:

• Bachelor’s degree in computer science, Statistics, Mathematics, Engineering, or a related quantitative field.

Professional Qualifications / Membership to professional bodies/ Publication:

• Practical ML certifications (e.g. Coursera, Udacity, DataCamp).
• Python, R, or SQL proficiency certifications.
• Cloud ML certifications (AWS, Azure, GCP) are a plus.

Work Experience Required:

• 2–5 years in data science, machine learning, or predictive modeling roles

Key Competencies:

• Statistical & Machine Learning Knowledge: applies predictive modeling, classification, clustering, and other techniques to solve real problems.
• Programming Proficiency: proficient in Python, R, and SQL, with hands-on experience using data science libraries and frameworks.
• Data Wrangling & Feature Engineering: transforms raw data into meaningful inputs for models through cleaning, joining, and deriving features.
• Curiosity & Innovation: constantly seeks new methods, algorithms, and technologies to improve performance.
• Model Deployment & Monitoring: familiar with putting models into production, versioning, and tracking performance over time.
• Communication of Insights: translates complex analysis into clear, actionable insights tailored to the business audience.
• Collaboration & Teamwork: works effectively with analysts, engineers, and business teams to drive outcomes.
• Adaptability: quickly learns new tools and adjusts to changing priorities or technologies.