Minimum Academic Qualifications:

• Bachelor's or Master’s degree in Data Science, Statistics, Computer Science, Artificial Intelligence, or a closely related quantitative field.
  

Experience:

• Applicants should possess at least 5 years of progressive experience in data science, advanced analytics, or applied research roles, with demonstrated responsibility for complex analytical or modelling tasks.
  

Data Pipelines and Reporting

• Contributes significantly to the creation of the data architecture in terms of projected and expected data needs, performance and efficiency KPIs.Scopes and stages work into well-defined milestones to avoid monolithic deliverable.
  
• Go-to expert in are or the codebase. Understands architecture of the entire systems and provides technical advice and weights on the technical decisions that impact whole project.
  
• Able to successfully design and build end-to-end solutions with guidance from experts in the fields.
  

Data Science Strategy and Planning

• Contribute to the development and implementation of data science strategies.
  
• Work with cross-functional teams to identify and prioritize data science requirements.
  
• Support in recommending and implementing new technologies to enhance data science capabilities.
  

Data Quality and Governance

• Ensure the accuracy and reliability of data through data profiling, cleansing, and validation.
  
• Collaborate with data governance teams to establish and maintain data quality standards.
  
• Acquire data from primary or secondary data sources, filter, and clean data, maintain databases/data systems, and ensure data quality.
  
• Research on governance trends, best practices and improves on existing implementations. Constantly looking for improvements on the previous iterations.
  

Advanced Analytics and Modeling

• Model, design, and implement AI algorithms using diverse sources of data.
  
• Design and implement rigorous evaluation pipelines for AI models including large language models (LLMs), retrieval-augmented systems, and task-specific models.
  
• Support in the development and maintenance of benchmarking datasets (e.g. agricultural Q&A, edge cases, contextual prompts) to support standardized model assessment.
  
• Lead technical safety testing of AI advisory systems, including hallucination detection, inappropriate content identification, and escalation logic.
  
• Support the development and testing of guardrails, disclaimers, and fallback mechanisms for farmer-facing advisory use cases.
  
• Design and analyse experiments (e.g. A/B testing, persona-based trials) to assess AI output quality, usability, and performance across different contexts.
  
• Work closely with Data Engineers and MLOps Engineers to ensure AI pipelines are reproducible, auditable, and well-documented.
  
• Explore, learn, and deliver more complex tasks, including robust scheduled code execution, building frameworks and Apis for the rest of the team and building eventbased data processing
  

Collaboration and Stakeholder Management

• Collaborate with internal and external stakeholders to gather business requirements and understand analytical needs.
  
• Provide support and training to end-users on utilizing data science tools and interpreting analytical outputs.
  
• Support in writing reports, documentations, and publications related to business intelligence.
  
• Liaise and work effectively with the software development team to ensure all data needs are well addressed in projects.
  
• Research new and emerging trends in data science to grow skills and facilitate client projects.
  

Project Management and Follow-up

• Achieve results through follow-up of projects through to completion.
  
• Monitor project progress, manage priorities, commit to achieving quality outcomes, adhere to documentation procedures, and seek feedback from stakeholders to gauge satisfaction.