Research Data Specialist

Responsibilities

• Monitor system performance, compute resources, and data storage costs to optimize resource utilization.
• Biological Data & Code Lifecycle Management.
• Systematically manage, curate, and version biological experimental data (such as genomic, RNA-seq, data) along with accompanying bioinformatics software, scripts, and pipelines.
• Establish standardized code repositories, version control workflows, and documentation best practices for research teams.
• Pipeline Support & Integration.
• Collaborate with local and global IT, AI Engineering, and Bioinformatics teams to align data storage and computing standards.

Knowledge, Experiences & Capabilities Knowledge

Strong knowledge of modern data storage, data warehousing, and cloud computing platforms (e.g., AWS S3/EC2, Snowflake, or equivalent enterprise systems). Deep familiarity with Linux/Unix operating systems, high-performance computing (HPC) cluster environments (e.g., Slurm, SGE), and job scheduling mechanics. Understanding of database systems (SQL) and data engineering workflows for managing structured and unstructured data. Basic understanding of popular high-throughput biological data types. Backgrounds of plant biology, biotechnology research, and biological experimental workflows is preferred. Education and Experience Ph.D. or Master’s degree in Bioinformatics, Computational Biology, Computer Science, Data Engineering, Information Technology, or a related quantitative discipline. 3+ years (for Ph.D.) or 5+ years (for Master’s) of experience in managing high-throughput data, HPC clusters, or enterprise cloud data architectures in academic or industry research environments. Proven track record of managing large-scale datasets, building data pipelines, and establishing code repository architectures. Demonstrated experience working on cloud infrastructure (Snowflake or equivalent platforms). Experience in agriculture, biotechnology, life sciences, or related research-intensive industries is preferred. Capabilities Proficiency in Python, Bash/Shell scripting, and SQL, with strong software development best practices (version control, containerization, documentation). Ability to architect, migrate, and optimize data workflows across HPC cluster servers and modern cloud platforms (AWS, Snowflake). Strong problem-solving skills with a focus on data governance, data integrity, and reproducible computational research. Ability to communicate complex technical, cloud, and computational infrastructure concepts effectively to experimental scientists and non-technical stakeholders.

Demonstrated ability to establish productive cross-functional collaborations across bioinformatics, software engineering, and laboratory teams. Excellent written and spoken English

Note : Syngenta is an Equal Opportunity Employer and does not discriminate in recruitment, hiring, training, promotion or any other employment practices for reasons of race, color, religion, gender, national origin, age, sexual orientation, gender identity, marital or veteran status, disability, or any other legally protected status. To learn more visit: www.syngenta.com

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