Responsibilities
Partner with R&D scientists to develop and prototype rigorous machine learning solutions aligned to project needs
Design and implement scalable data pipelines for processing high-complexity datasets such as high-throughput bioassays or large-scale agriculture datasets
Partner with data scientists, data engineers, and production teams to deploy and maintain data products at scale
Communicate and train research partners on models and products to facilitate data-driven decisions
Communicate insights derived from complex data analysis into simple conclusions that empower leadership to drive action; communicate results in internal and external forums; and contribute to scientific articles as needed
Steward data product life cycle and partner with other scientists to continuously improve underlying models and optimize data architecture
Stay abreast of emerging technologies in big data, machine learning, and agriculture tech and advocate for their adoption where beneficial
Educational Qualifications
M.S. or above in Applied Statistics, Artificial Intelligence, Biostatistics, Computer Science, Data Engineering, Data Science, Engineering, Machine Learning, Physics, Software Engineering, or related highly quantitative fields. Ph.D or additional years of experience preferred but not required.
Required Qualifications
Strong expertise in R or Python programming languages and their application to data wrangling, machine learning (e.g., TensorFlow, PyTorch), and data visualization
Experience and fundamental understanding of machine learning techniques (e.g., logistic regression, random forest, XGBoost, SVMs, K-means, neural networks)
Solid understanding of variable selection; dimensionality reduction; model diagnostics; and model training, testing, and validation
Experience deploying machine learning models in production (e.g., CI/CD pipeline development; containerization using tools such as docker, podman, or Kubernetes; Git)
Ability to work both independently and within a multidisciplinary team environment to provide innovative solutions
Ability to successfully collaborate with colleagues from diverse technical backgrounds which includes excellent communication, interpersonal, verbal, and written skills
Strong critical thinking and problem-solving skills, flexibility, and willingness to learn
Preferred Qualifications
Familiarity with modeling biological, cellular, or ecological data; molecular biology or biochemistry concepts; or data science in agriculture
Proven experience as a machine learning engineering or similar role with a strong focus on machine learning deployment and data pipeline construction
Familiarity with artificial intelligence or generative AI techniques
Experience in big data technologies (e.g., Hadoop, Spark) and database management systems (e.g., SQL, NoSQL)
Experience with AWS
Experience consulting on scientific projects or working within a scientific team
#J-18808-Ljbffr
