Job Description
Join the transformative team at Google's 'Debug' initiative, where cutting-edge technology meets global health. We are looking for a dedicated Life Sciences Research Associate to contribute to our mission of characterizing mosquito bionomics and optimizing mass rearing performance.
In this role, you will be at the intersection of entomology, data science, and public health impact. You will work within a world-class environment designed to tackle some of the world's most challenging biological problems. If you are passionate about applying rigorous scientific methods to large-scale biological projects and thrive in an interdisciplinary team, this is the perfect opportunity to make a tangible difference.
You will be responsible for meticulous data collection, laboratory experimentation, and the continuous improvement of rearing processes. At Google, you will have access to unparalleled resources, fostering an environment where innovation is the standard.
Responsibilities
- Execute longitudinal studies to characterize mosquito bionomics, including lifecycle analysis and survival rates.
- Manage and maintain mass-rearing colonies, ensuring optimal health and standardized performance metrics.
- Design and conduct controlled laboratory experiments to evaluate biological performance under varying conditions.
- Collect, clean, and analyze high-quality experimental data to support strategic decision-making.
- Collaborate with interdisciplinary teams including data scientists, engineers, and public health experts.
- Document methodologies and research findings for internal review and publication.
- Develop and implement standard operating procedures (SOPs) for large-scale biological rearing processes.
Qualifications
- Bachelor’s or Master’s degree in Biology, Entomology, Life Sciences, or a related field.
- Minimum of 2-3 years of research experience in a laboratory or agricultural setting.
- Strong understanding of insect biology, particularly mosquito life cycles and vector behavior.
- Proficiency in experimental design and statistical analysis software (e.g., R, Python, or JMP).
- Excellent documentation skills and attention to detail when handling large datasets.
- Ability to work effectively in a fast-paced, collaborative, and innovation-driven environment.
- Strong problem-solving skills with a focus on process optimization and efficiency.