Sustainable Development GoalsSDG 2. ZERO HUNGER
Come work alongside some of the most talented minds in the agtech industry. We are a team of innovators who want to make an immediate and significant impact. You will be given the opportunity to work with an amazing group of people who care about each other and their work.
What we do:
At Arable, our goal is to provide all the world’s farms with the optimum-quality data and predictive insights that will revolutionize the global food system. To achieve this we are uncomfortably ambitious, combining agronomic, data and sensor science with software and hardware technology to change the way farming data is collected, processed and analyzed. To be successful, we are recruiting the best possible team with the ability to turn brand new science and technology into products that solve real world problems. It is an ambitious goal, but the need has never been greater to rethink how we will feed a growing world population and reduce our impact on natural resources and the environment. We hope our work will impact the lives of farmers everywhere, improving their lives and contributing to global food security in the decades to come.
A few examples of the work we’re doing today:
- Helping farmers in India and China through improved insights into crop development
- Giving produce growers in California the tools to optimize production with less waste
- Helping irrigated farmers in Nebraska manage water more efficiently and sustainably to protect our water supply
What we are looking for:
We are looking for an agronomist to join our growing team to work at the intersection of plant agronomy and data science. The right candidate is someone who can interpret and analyze farm data in new ways to create actionable farm insights, propose adjustments to historical/current farming practices, and make recommendations that enable and create the next generation of farming practices.
What you will do:
- Work with the data science team to develop new insights for crop irrigation, crop protection, and crop fertilization.
- Work closely with software engineers & data scientists to incorporate agricultural models into production machine learning and deep learning frameworks.
- Derive canonical representations of agronomy-related data, such as field events, soil types, weather events, etc., that are suitable for consumption by statistical and machine learning models.
- Work with data science, field experiment, and senior teams to support a broad spectrum of agronomy experiments.
What you bring:
- Ph.D. or Master’s Degree in Agronomy or related field.
- Good understanding of crop physiology.
- Demonstrated ability to self-manage, take initiative, problem-solve, and navigate through ambiguity.
- Experience designing field experiments: setting protocols to evaluate hypotheses; characterizing, sampling, and adjusting for environmental conditions (e.g. soil, climate, etc.), and measuring impacts on outcomes of interest, such as yield, quality, water usage, etc.
- Experience analyzing using Python and machine learning to design crop models.
- Experience running agricultural models such as APSIM, DSSAT, or DNDC at scale.
- Ability to communicate effectively both in writing and verbally to various audiences.
- Understand the significance of errors and omissions and experience in addressing these problems.
- Experience with PostgreSQL and PostGIS.
- Familiarity with commercial agricultural conditions and practices.
What we offer:
At Arable you will be joining a company of dedicated team players who bring together diverse expertise and a passion for building a more sustainable future. We are a fast-moving startup committed to providing a rewarding employee experience through the work we do, the team, compensation, and benefits including:
- Excellent medical, dental, vision, life, disability benefits, and a 401k program
- Flexible PTO
- A focus on community involvement and career development
- Being an intricate part of creating an excellent IoT product in the agtech space and having a positive impact on the world we live in.