THE ORGANIZATION
The Alliance of Bioversity International and CIAT delivers research-based solutions that harness agricultural biodiversity and sustainably transform food systems to improve PEOPLE'S lives. Alliance solutions address the global crises of malnutrition, climate change, biodiversity loss, and environmental degradation.
The Alliance works with local, national and multinational partners across Latin America and the Caribbean, Asia and Africa, and with the public and private sectors. The Alliance is part of CGIAR, a global research partnership for a food-secure future, dedicated to reducing poverty, enhancing food and nutrition security, and improving natural resources and ecosystem services.
About the position
The Senior Data Systems Associate will be a key role within the Artemis project, contributing to the efficient management and utilization of data for the development of AI-powered phenotyping tools. This position involves a range of tasks, including data organization, quality control, processing, and annotation, requiring meticulous attention to detail and a strong understanding of data management principles. He/She will work closely with the Data Systems Engineer and the broader AI/ML team to ensure the smooth flow of high-quality data, ultimately supporting the project's goal of accelerating crop improvement efforts and enhancing food security across Africa, Latin America, and Asia.
Key duties & responsibilities
Data Management
- Support the development, implementation, and continuous improvement of the project's data management strategy.
- Support the organization, integration, and governance of datasets from diverse sources, including field trials, image repositories, partner institutions, and digital platforms.
- Contribute to the development and maintenance of comprehensive data documentation, metadata standards, and data dictionaries to ensure long-term usability and reproducibility.
Data Quality Control:
- Design and implement robust data quality control procedures.
- Support data cleaning, validation, and verification tasks to ensure data accuracy and consistency.
- Identify and report data anomalies and inconsistencies.
Data Processing and Preparation:
- Lead the design, development, and optimization of data processing pipelines for machine learning and analytical workflows.
- Manage data storage, transformation, and integration solutions, ensuring scalability, security, and interoperability.
- Guide efforts to convert data between different formats as needed.
Data Annotation and Labeling Oversight:
- Provide technical guidance for the annotation and labeling of images and structured data for supervised machine learning tasks.
- Collaborate with domain experts and model developers to define labeling schemas, annotation workflows, and quality assurance protocols.
- Evaluate and recommend tools, platforms, and processes to improve annotation efficiency and accuracy at scale
Capacity Building and Collaboration
- Provide guidance to junior data staff and contributors involved in data collection, management, and processing activities.
- Develop training resources and lead capacity-building initiatives on data management best practices and tools for internal teams and external partners.
- Serve as a key liaison between data, research, and engineering teams to ensure coordinated and efficient data flows that support modeling, analytics, and decision-making.
- Represent the data function in strategic planning meetings, reporting processes, and donor communications.
Requirements
- Master's degree in Computer Science, Data Science, or a related field.
- 3+ years of experience in data management, data analysis, or a related field.
- Familiarity with data quality control and data processing techniques.
- Experience with data annotation and labeling tools.
- Proficient programming skills in Python or other relevant languages.
- Proficiency in data manipulation and analysis using coding.
- Strong attention to detail and accuracy.
- Excellent communication and interpersonal skills.
- Ability to work independently and as part of a team.
- Experience with agricultural and/or plant sciences.
- Knowledge of image processing and analysis techniques.
- Familiarity with metadata standards and best practices.
Terms of employment
This position is a nationally recruited position based in Arusha, Tanzania. The initial contract will be for 1 year subject to a probation period of 3 months and is renewable depending on performance and availability of resources.
This position is graded at BG07, level, with a minimum basic salary of TZS 3,929,704 in a scale of BG01 to BG14 (BG14 being the highest level according to the Alliance job classification framework policy). We offer a competitive salary and excellent benefits including but not limited to insurance, retirement plan, staff training and development, paid time off and flexible working arrangements.
The Alliance Bioversity-CIAT is committed to fair, safe, and inclusive workplaces. We believe that diversity powers our innovation, contributes to our excellence, and is critical for our mission. Recruiting and mentoring staff to create an inclusive organization that reflects our global character is a priority. We encourage applicants from all cultures, races, colors, religions, sexes, national or regional origins, ages, disability statuses, sexual orientations, marital status, and gender identities. Female candidates are strongly encouraged to apply
Applications
Applicants are invited to visit https://alliancebioversityciat.org/careers to get full details of the position and to submit their applications. Applications MUST include reference number RFP300575
- Master's degree in Computer Science, Data Science, or a related field.
- Provide guidance to junior data staff and contributors involved in data collection, management, and processing activities.
- Provide technical guidance for the annotation and labeling of images and structured data for supervised machine learning tasks.
- Lead the design, development, and optimization of data processing pipelines for machine learning and analytical workflows.
- Design and implement robust data quality control procedures.