Staff Data Scientist, Sustainability
Staff Data Scientist, Sustainability
This job is no longer open
About Indigo
Indigo Ag is a mission-driven company dedicated to harnessing nature to help farmers sustainably feed the planet. We innovate across a breadth of technology and science disciplines to help enhance agriculture's long-term sustainability and profitability and mitigate the climate crisis. We seek to positively impact the world through the digital and biological solutions we bring to the market, creating a more resilient earth, healthier economies, and thriving communities. Our people build partnerships, explore the science, and engineer the technology to help grow the future of sustainable agriculture. The only way to achieve it is through a unique combination of expertise – from Fortune 500 to academia, soil science and agriculture, to tech start-ups.
About the team:
The Sustainability Data Science team is a multidisciplinary team that harnesses data science, statistics, and technology to build a data-driven, scientifically-rigorous sustainability program. Our activities span several aspects including: operational tasks to conservatively quantify Carbon sequestration approaches, rates, and efficacy partner with our science team on more R&D activities to design, analyze, and derive actionable insights from the frontiers of sustainability and carbon-sequestration field experiments and sensor technologies strategic decision support with forecasting and scenario planning partner with the product team on tools, processes, and data products to enhance the grower’s Carbon and Source program experience.
About the role
We are looking for a staff-level data scientist to lead growth and engagement activities within the Sustainability Data Science team. The success of the Carbon program will hinge, in part, on top-of-funnel success. To expand the program and maximize our impact to the business and to the environment will require finding not just growers but the right growers. That is, those who are interested in regenerative practices but who face barriers that prevent them from adopting those practices. Who and where are the growers we can help transition to regenerative practices? What do they have in common? Where can we find more? How can we find and reach them? For those that we acquire, what is their lifetime value (LTV) and churn risk? How do we keep them engaged and successful?The data will span transactional data, web analytics, as well as a rich set of open and proprietary agronomic and demographic datasets. As such, analyses may span questions around growth, engagement, and agronomics insights at a variety of spatial scales. This role will encompass typical growth and engagement data science activities such as churn prediction, LTV, lookalike modeling, lead scoring, segmentation, session analysis, and experiments partnering with a range of stakeholders across commercial, marketing, product, tech, data, and data science. It will also involve building personalized data products collaborating with product, tech, and other data scientists on the team.
Competencies
Proven ability to independently translate needs into Data Science problems by asking the right questions and delivering solutions by identifying necessary data, building models, and producing actionable results.5+ years practical industry experience in supervised and unsupervised machine learning including, regression, classification, time series forecasting, feature selection, evaluation, model selectionStrong expertise in data wrangling / munging (we work mostly with Python and SQL)Excellent communication, consulting, and story-telling skills,and able to work well with a range of stakeholders with varying backgrounds and levels of technical expertise.Mentor more junior members and contribute to the overall culture, standards, and processes of the team.Strong sense of quality and attention to detailCurious and proactive. Bias for action.
What will you do?
Lead, coach, and mentor junior data scientistsReview machine learning models and codeWork with our product managers and business stakeholders to identify and scope the highest value data science problemsExplore and understand our data and how they can feed production-grade machine learning modelsProactively utilize data and business understanding for feature engineeringBuild, prototype, and deploy state-of-the-art machine learning models to solve real business problemsCommunicate, interpret, and explain modeling output to stakeholdersMeasure value of the machine learning models we deploy
Preferred Qualification:
Masters Degree or PhD in Computer Science, Math, Statistics, Physics, Engineering, or related quantitative fieldExperience in a startup environmentExperience working with geospatial data is a plus, as is experience building growth engagement models.
#LI-Remote
At Indigo, we are committed to providing an environment of mutual respect where equal employment opportunities are available to all applicants and team members. All employment decisions are based on business needs, job requirements, and individual qualifications, without regard to race, color, religion or belief, sex, pregnancy (including childbirth, lactation, and related medical conditions), national, social or ethnic origin, age, physical, mental or sensory disability, marital status, family or parental status, sexual orientation, gender identity and/or expression, family medical history or genetic information, military and veteran status, and any other characteristic protected by applicable law. Indigo believes that diversity, equity, and inclusion among our team members are critical to our success. We seek to recruit, develop and retain the most talented people from a diverse candidate pool.
If you’re applying for a job in the U.S. and need reasonable accommodation for any part of the employment process, please email talent@indigoag.com and let us know the nature of your request and contact information. Please note that only those inquiries concerning a request for reasonable accommodation will be responded to from this email address.
This job is no longer open