Senior Data Scientist

A top employer is now accepting applications for this role. Basic job summary:

• The role is responsible for leading advanced data science workstreams related to AI model development, benchmarking, safety testing, and applied analytics. will involve implementing data ingestion frameworks,

Duties & Responsibilities: 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-d...