About the Role
Lead the design and implementation of machine learning models to solve complex business challenges, mentor junior analysts, and collaborate across departments to integrate AI-driven insights into operational workflows.
Responsibilities
- Develop scalable machine learning pipelines for real-time data processing
- Translate business requirements into analytical frameworks
- Evaluate model performance using statistical validation techniques
- Optimize algorithms for accuracy, speed, and resource efficiency
- Collaborate with engineering teams to deploy models into production
- Monitor deployed models for performance degradation and data drift
- Document methodologies and maintain version-controlled code repositories
- Present technical findings to non-technical stakeholders
- Stay current with advancements in AI and machine learning research
- Ensure compliance with data privacy and security standards
- Lead ad hoc analytical projects with cross-functional impact
- Design experiments to test hypotheses and validate assumptions
- Mentor team members in best practices for data modeling
- Integrate external data sources to enrich internal datasets
- Support data quality initiatives and metadata management
- Contribute to the selection of analytics tools and platforms
- Identify automation opportunities in reporting and analysis
- Work closely with product teams to embed intelligence features
- Apply natural language processing techniques to unstructured data
- Utilize cloud-based machine learning services for deployment
- Implement A/B testing frameworks for model comparison
- Drive standardization of model development life cycle processes
- Assess ethical implications of algorithmic decision-making
- Coordinate with compliance officers on regulatory requirements
- Facilitate knowledge sharing through internal workshops
Compensation
Competitive salary and benefits package
Work Arrangement
Hybrid work model with flexible scheduling
Team
Collaborative data science and analytics team
About the Team
This role operates within a specialized analytics unit dedicated to transforming agricultural data into actionable intelligence. The team emphasizes innovation, rigorous methodology, and practical application of AI in food systems.
Technology Stack
Primary tools include Python, TensorFlow, PySpark, BigQuery, Kubernetes, and Vertex AI. Infrastructure is hosted on Google Cloud Platform with automated CI/CD pipelines for model deployment.
Available for qualified candidates
