Description

Who are we, and what do we do?

At Corteva Agriscience, you will help us grow what’s next. No matter your role, you will be part of a team that is building the future of agriculture – leading breakthroughs in the innovation and application of science and technology that will better the lives of people all over the world and fuel the progress of humankind.

Are you creatively inspired by the latest arXiv paper on a novel deep learning approach? Does your mind abound with modeling possibilities when you learn about a new type of data or scientific field? Are you interested in making real-world impacts to global agriculture? If you answered yes to these questions, you might be a good fit for our Research Scientist position in Advanced Phenotyping Analytics. 

We are looking for a self-motivated, curiosity-driven scientist to work at the forefront of deep learning and computer vision. You will directly collaborate with innovators in plant breeding, automation, genetics, and biostatistics to build the future of Corteva. By applying innovative analytics to multifaceted sources of data from rovers, drones, and satellites, you will have a direct role shaping how global agriculture responds to the demands of a growing population and changing climate.

Responsibilities:

How will you help us grow? It matters to us, and it matters to you!

Join and engage with a small but highly collaborative team focused on delivering state-of-the-art analytics solutions to problems encompassing phenotyping, breeding advancement, product placement, and genome editing

Engaging with scientists from multiple domains to identify where the application of machine learning or deep learning would be appropriate for their data and problem spaces, including proposing new opportunities as they arise due to new developments in data acquisition or analytical techniques

Identifying and acquiring necessary datasets, conducting feature engineering and data QC appropriate for the methodology and customer needs, validating results and iterating approaches as needed

Prototyping and optimization of analytical pipelines, for both analytical performance and computational efficiency

Communication with stakeholders of diverse technical backgrounds on the methods used and the results obtained

Working with developers and machine learning engineers in guiding models from development into production

Qualifications

Requirements:

What expertise have you grown? What do you bring to the table?

Strong history of independent scientific problem solving, from hypothesis generation to construction and deployment of appropriate analytics to interpretation and presentation of the results for others

A track record of continual skill development in analytical methods and deployment platforms, through consultation of the current literature and engagement with the data science community

Strong foundation in Python programming and Linux/Unix-based systems

Practical and theoretical expertise in deep learning, machine learning, and statistics, with a heavy background in computer vision, construction of latent/embedding spaces, and time-series analysis

Extensive development experience using one or more computational graph frameworks (e.g. Torch, Tensorflow, JAX)

Practical knowledge and experience with cloud-computing systems and platforms, including the routine deployment of pipelines through Kubernetes

Proven track record of producing organized, documented, and version-controlled code

Excellent written and verbal communication skills, with the ability to work as both part of a working team or as a project leader

Advanced degree (M.S. or Ph.D.) in data science, computer science, physics, mathematics, statistics, or another field with heavy data analytic requirements 

    What will generate excitement:

Domain knowledge in plant breeding, agronomy, biological systems, quantitative genetics, or genomics

Expertise in a broad set of machine learning and deep learning paradigms, including unsupervised-learning, self-supervised learning, semi-supervised learning, active learning, and reinforcement learning

Experience with sensor fusion, using computer vision for regression, and applying computer vision to novel instance and semantic segmentation problems

A strong background in probabilistic methods, including the use of probabilistic programming languages such as Edward or Pyro

Experience deploying user-facing applications using frameworks such as Dash/Plotly or RShiny

Community:

We care about you and we care that you're comfortable. While there's no place like home, Corteva comes close.

Benefits:

Let’s peek at how you can grow your wellbeing, health, and future at Corteva!

•    Strike a better worklife balance with robust time off benefits including paid maternity, paternal and family illness leave

•    Prepare for your future with our competitive retirement savings plan, tuition reimbursement program, and more

•    Enjoy access to health benefits for you and your family on your first day of employment

•    And much, much more!

Ready to grow your perspectives, impact and career? Start by applying to this opportunity today!