Does this sound like you?

• Bachelor’s degree in Computer Science, Statistics, Applied Mathematics, Engineering, or a related field.
  
• Minimum of 5 years of hands-on experience developing and deploying data science or AI/ML solutions in a business setting, with a proven track record in AI strategy and implementation.
  
• Demonstrated expertise in designing, prototyping, and deploying machine learning models, including generative AI applications and critical models like Credit Scoring.
  
• Strong interpersonal and communication skills, with the ability to translate complex AI solutions into clear business value for non-technical stakeholders.
  
• Strategic thinker with an innovative mindset, driven by curiosity to uncover new opportunities through self-discovery and a strong sense of ownership to ensure high-quality outcomes.
  
• Creative problem-solver who thrives in dynamic environments, prioritizes efficiency, and goes above and beyond to propose and implement innovative solutions.
  

Required Skills

• Data Science & Machine Learning: Proficiency in Python and ML frameworks for developing and deploying machine learning algorithms that drive business value.
  
• Generative AI & LLMs: Expertise in applying generative AI and large language models to create innovative applications, automate tasks, and enhance self-service data platforms.
  
• SQL Expertise: Strong skills in SQL for data manipulation, querying, and analytics in relational databases and data warehouses (e.g., ClickHouse).
  
• MLOps and Deployment: Proficiency in MLOps tools (e.g., MLflow, Kubeflow) and Docker for packaging ML applications; experience with cloud platforms (e.g., AWS SageMaker, GCP Vertex AI, Azure ML) and CI/CD pipelines (e.g., GitHub Actions).
  
• Version Control and Deployment: Proficiency in Git/GitHub for collaborative development and Docker for containerizing data applications.
  
• Prototyping Tools: Experience with front-end prototyping tools (e.g., Streamlit, Gradio) for rapid demo development.
  
• Collaboration and Communication: Exceptional ability to align AI initiatives with business goals and communicate complex solutions to diverse stakeholders.
  
• BI Tools: Familiarity with BI tools (e.g., Power BI, Tableau, Amazon QuickSight) for supporting analytics and visualization needs.
  

• AI and Data Science Leadership: Own the end-to-end lifecycle of AI/ML projects, from ideation and business scoping to prototyping, model packaging (e.g., via Docker), and deployment with DevOps, ensuring seamless integration into production environments.
  
• Predictive Analytics and Model Ownership: Own and enhance critical ML models, such as the Credit Scoring model, by continuously monitoring performance, ensuring high accuracy, and implementing improvements to boost predictive power and reliability. Develop data-driven predictive models to unlock revenue growth, enable smarter credit decisions, strengthen collections, and optimize marketing strategies, addressing business challenges and driving operational efficiency through innovative automation.
  
• Champion AI-Led Self-Service Data Platform: Spearhead the development and adoption of an AI-driven self-service data platform, leveraging generative AI, AI agents, and LLMs to automate tasks, streamline workflows, and empower teams to access insights independently.
  
• Drive Innovation through POCs: Proactively design and execute POCs using AI to test hypotheses, validate business use cases, and develop new models to address user needs or simplify processes through automation, collaborating with the Head of Data to align with strategic goals.
  
• Business Process Optimization: Partner with business teams (via the Data Business Partner) to identify repetitive, decision-heavy, or complex tasks, applying predictive models or AI-driven automation to optimize processes and deliver measurable value.
  
• MLOps and AI Governance: Collaborate with Data Engineers and DevOps to build robust ML pipelines, ensuring model monitoring, versioning, and reliability. Maintain AI fairness, performance, and compliance, particularly for region-specific use cases like credit scoring or customer segmentation, adhering to local regulations (e.g., Kenya’s Data Protection Act).
  
• Cross-Functional Collaboration: Work with department heads and stakeholders to understand challenges and align AI-driven solutions with strategic objectives, fostering trust through clear communication and impactful deliverables
  
• Continuous Improvement: Driven by curiosity and innovation, identify and implement enhancements to data science processes, model accuracy, and analytics workflows (e.g., streamlined data extraction or optimized algorithms). Take ownership of initiatives, ensuring follow-through to completion and delivering solutions that enhance efficiency and data trust.
  
• Ad Hoc Support: Address occasional ad hoc requests, such as building one-off dashboards or data extracts, to support the Business Intelligence team with timely, accurate solutions.