Lead Data Science and AI at Wema Bank Plc

Wema Bank offers a range of retail and SME banking, corporate banking, treasury, trade services and financial advisory to its ever-expanding clients. In 2009, the Bank underwent a strategic repositioning exercise which culminated in a decision to operate as a commercial Bank with regional authorisation in South-South Nigeria, South-West Nigeria, Lagos and Abuja in 2011. Operating a network of over 125 branches and service stations backed by a robust ICT platform across Nigeria, we are committed to long-term sustainability in our business whilst maintaining the highest standards of social responsibility, corporate governance and diversity in our operations.

The Lead, Data Science and AI is responsible for providing strategic and technical leadership to the Data Science and AI team. The role oversees the end-to-end development and deployment of machine learning and artificial intelligence models to solve complex business problems and drive data-informed decision-making.

Job Details

• Provide technical leadership to the Data Science and AI team, mentoring team members and fostering continuous learning. 
• Supervise data scientists, assign tasks, review deliverables, and conduct performance appraisals. 
• Drive end-to-end development and deployment of ML models, ensuring alignment with business objectives. 
• Oversee data collection, cleaning, and preprocessing of structured and unstructured data for advanced analytics. 
• Lead the design and implementation of feature engineering pipelines to enhance model performance. 
• Manage the development of ML and AI models, including regression, classification, clustering, and deep learning, while ensuring scalability and accuracy. 
• Conduct advanced hyperparameter tuning, cross-validation, and model optimization techniques. 
• Collaborate with cross-functional teams to integrate ML solutions into production systems, ensuring seamless deployment and maintenance. 
• Manage the lifecycle of ML models, including versioning, retraining, and monitoring for performance.
• Guide the team in adapting and fine-tuning large language models (LLMs) for domainspecific use cases (e.g., chatbots, sentiment analysis, summarization). 
• Optimize ML/AI workloads using Azure Machine Learning (AML) and Azure AI services for cost and performance efficiency. 
• Implement and manage CI/CD pipelines for ML systems using Azure DevOps or other automation tools. 
• Act as a key liaison between data science, engineering, and business teams to prioritize high impact projects. 
• Develop strategies to address domain-specific challenges such as fraud detection, credit scoring, and personalized customer recommendations. 
• Translate complex business problems into technical deliverables and ensure timely project delivery

Requirements

PROFESSIONAL COMPETENCIES 

• Proven expertise in AI/ML techniques, including traditional models (regression, classification, clustering) and advanced models (ensemble methods, neural networks, reinforcement learning). 
• Hands-on experience in feature engineering, ETL processes, and managing large-scale data pipelines. 
• Proficiency in cloud-based ML platforms, particularly Azure Machine Learning, Azure Data Lake, and Azure Kubernetes Service (AKS). 
• Strong knowledge of tools like MLFlow for model tracking, versioning, and lifecycle management. 
• Ability to interpret and present technical insights to non-technical stakeholders, driving datainformed decisions.
•  Deep understanding of domain-specific challenges and trends in banking (e.g., churn prediction, customer segmentation, fraud detection). 
• Advanced programming skills in Python (preferred), R, or Scala, and familiarity with frameworks like Scikit-learn, TensorFlow, PyTorch, and Hugging Face Transformers.
•  Experience with visualization tools ( Matplotlib, Seaborn) and databases (SQL, NoSQL, Azure Cosmos DB).
•  Oversee all existing production models.
•  Expertise in leading technical teams, fostering innovation, and driving impactful data science projects.

QUALIFICATION AND SKILLS

•  Below are qualifications and skills required to work as a Data Scientist Qualifications include:
•  Bachelor’s degree in Engineering, Mathematics, Statistics, Computer Science, or related fields. Master’s degree in Data Science, AI, or a related field is a plus.
•  Certifications in AI/ML or cloud platforms (e.g., Azure, AWS, GCP) are advantageous. Skills Programming: Proficiency in Python, R, or Scala. Frameworks: TensorFlow, PyTorch, Scikit-learn, Hugging Face Transformers. DevOps: 
• Experience with Git, Azure DevOps, or Jenkins.
•  Cloud Platforms: Expertise in Azure AI/AML services and AWS, GCP etc Generative AI (LLMs)(Open AI, Gemini, Deepseek, etc) ,LangChain, Hugging Face Transformers, Prompt Engineering, Vector Databases (FAISS, Pinecone), Retrieval-Augmented Generation (RAG), and Azure OpenAI. Education: 
• Bachelor’s or master’s degree in data science, Computer Science, Statistics, Mathematics, or related field. 3+ years of professional experience in data science, analytics, or applied AI (preferably in banking or fintech). 
• Proven track record of developing and deploying machine learning models at scale. Solid understanding of financial services data (customer, credit, digital, and marketing analytics). Experience leading a technical team and managing multiple concurrent projects.

Method of Application

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