Senior Specialist - Data Science AI ML and Data Engineering.Enterprise Business Unit

•To lead AI/ML and data engineering initiatives to develop innovative AI-driven products and enhance business value.   •To own and implement AI/ML strategies, ensuring alignment with organizational goals, and drive the delivery of solutions that directly impact revenue generation.

Strategy Input and Implementation

•Contribute to the strategic vision for AI/ML and data engineering, ensuring alignment with business goals. •Identify opportunities where AI/ML can create competitive advantages, enhance customer experience, and drive revenue growth. •Guide the adoption of emerging AI trends, including generative AI and LLMs, to maintain technological leadership. •Partner with business leaders to define key AI/ML initiatives that align with corporate objectives. •Stay ahead of industry trends, ensuring the company remains a leader in AI/ML applications. •Research and prototype new AI techniques, particularly in Generative AI and Large Language Models (LLMs). •

 AI/ML Model Development & Optimization

•Design, develop, and implement AI/ML models to support predictive and prescriptive analytics. •Continuously refine AI methodologies to improve efficiency and accuracy. •Optimize AI algorithms for performance, scalability, and real-world applicability. •Ensure AI models are explainable, bias-free, and aligned with ethical AI principles. •Deploy models in production environments across cloud platforms (GCP, AWS, Azure, Oracle Cloud). •

Data Engineering & Infrastructure Management

•Oversee data pipelines, ensuring efficient data collection, cleaning, and transformation. •Develop scalable data architectures to support AI/ML workloads. •Implement automation tools to streamline data processing and model training workflows.   Forecasting & Business Intelligence •Develop forecasting models that improve decision-making and business planning. •Leverage AI for real-time analytics and business insights. •Implement AI-driven solutions for customer behavior prediction, market trends, and operational efficiency. •Use AI to analyze competitor data, identifying industry trends and market gaps. •Provide insights that inform product development, pricing, and go-to-market strategies. •Benchmark AI/ML capabilities against industry leaders to maintain competitive positioning.

Budget, Cost Control & Reporting

•Optimize operational expenses while maximizing revenue generation. •Provide regular financial reporting and forecasting on platform performance. •Manage, monitor and control the budgetary needs for Programmes/ Projects in line with business objectives. •Manage and optimise the budget, ensuring all expenditure is in line with the agreed budgets. •Monitor costs and determine initiatives to increase efficiencies and optimise resources - maximise cost/benefit ratios . •Where relevant, ensure Vendor and IT costs are effectively managed.   Data Visualisation and Storytelling •Develop dashboards and reports using Tableau, PowerBI, and Databricks to communicate AI-driven insights. •Present complex data in a clear and actionable manner to both technical and non-technical stakeholders. •Drive a data-driven culture within the organization by enabling access to AI-powered insights. •

Collaboration & Stakeholder Engagement

•Work closely with product, technology, and business teams to integrate AI solutions into company offerings. •Translate business problems into AI/ML-driven solutions, ensuring commercial viability. •Act as an AI/ML SME, providing guidance on best practices and ethical AI deployment.   Governance, Policies and Procedures •Ensure AI solutions comply with data privacy laws and ethical AI guidelines. •Implement monitoring mechanisms to track model performance and mitigate risks. •Establish governance frameworks for responsible AI deployment. •Develop and enforce governance policies and procedures to ensure compliance with corporate standards and regulatory requirements. •Implement risk management strategies to identify, assess, and mitigate business risks. •Maintain accurate and transparent documentation of business activities and decisions. •Conduct regular reviews and audits to ensure adherence to regulatory and governance policies.

People & Culture Management

•Enable and model healthy employee relations and collaborative teamwork. •Provide technical, procedural and policy guidance to staff, colleagues , partners and vendors. •Create and implement own personal development plans. •Manage own performance and identify training needs. Coach and guide colleagues. •Share deep domain knowledge and expertise with peers, helping them understand complex concepts and best practices in the Business Line, fostering knowledge transfer amongst peers. •Where required, lead formal and informal training sessions on specific platform management tools, processes, or methodologies. •Contribute to building a culture  of continuous evaluation and improvement. Drive a culture of high performance, accountability and consequence management. •Act as an ambassador for the team by living the values and vital behaviours and changing and influencing employees’  behaviour. •Make the environment the best place to work. Foster professionalism, loyalty and commitment to the organization. Build the Company’s brand to be the employer of first choice.

Qualifications: 

•Minimum of 4-year tertiary degree in related field (Data Science, Mathematics, Statistics, Computer Science) •Master’s degree is advantageous   Experience: 

• Minimum 5-6 years experience in data science with a bias to software engineering AI/ML
• Demonstrated experience in deploying AI solutions in cloud environments and integrating AI insights into business strategy.
• Experience working in a medium to large organisation
• Worked across diverse cultures and geographies advantages