Senior Data Scientist

AgZen is a fast-growing precision agriculture company headquartered in Somerville, MA, built on MIT research and focused on one problem: making crop spraying more efficient. Our flagship product, Real Coverage, is the world's first system that measures and controls droplet coverage at the leaf level, giving growers real-time visibility into spray performance and cutting chemical and water use by up to 50% without sacrificing yield.

We are a small, technically deep team working at the intersection of fluid mechanics, computer vision, AI, and real agricultural environments. If you want to build technology with measurable impact on how the world grows food, this is the place to do it.

We are looking for a sharp, tenacious, and thorough Senior Data Scientist to join our team. As part of the the perception team, you’ll be responsible for uncovering patterns from crop protection data collected from Real Coverage units installed on sprayers all around the world. Along with being a key player in our ML team, you will design dashboards and reports and develop a data-driven recommendation engine that enables further agricultural optimization.

This role will be an essential bridge between AgZen’s customer success and measurement groups. Strong communication, flexibility, teamwork, the desire to take on different responsibilities and own them will all be essential skills for a successful applicant.

This role is located in Somerville, MA (Boston area) with work required to be in-person. Travel to support AgZen's major manufacturing builds (seasonal) will be required.

• Perform historical data analysis of spray applications by identifying patterns and analyzing the impact of key factors including input parameters, rates, mixtures, agricultural practices, and environmental conditions
• Apply advanced statistical, machine learning, forecasting, optimization, and experimentation methodologies to solve complex agricultural data challenges
• Architect and operationalize machine learning solutions including AgZen’s Real Coverage Recommendation Engine
• Build tooling and support non-technical domain experts in understanding perception pipeline performance and identifying opportunities for pipeline improvement
• Drive data-centric ML model improvements to achieve critical AgZen milestones
• Define and implement scalable data quality measures across complex, multimodal data labeling pipelines
• Contribute to an organization wide data ontology and class structure for perception models
• Collaborate closely with cross-functional teams of software engineers, machine learning scientists, product specialists, and researchers to design, build, and maintain robust data pipelines grounded in sound data organization, domain knowledge, and careful analysis
• Communicate technical findings, data characteristics, and limitations clearly and effectively to both internal partners and external collaborators

Required:

• MS or PhD in Computer Science, Engineering, Statistics, Mathematics, Physics, or related field (or BS with equivalent work experience)
• Proficient using data query languages (SQL/postgreSQL) to quickly build complex yet efficient data queries at scale and using Python to build production-quality code
• Proficient in exploratory data analysis (EDA) and data visualization to understand and present trends and their implications for the business.
• Background in statistical modeling and analysis, including experience making data-driven decisions from physical sensor data
• Proven experience in the use of the main data-science, analytics, modeling and visualization Python libraries, including machine learning and deep learning
• Strong data-centric ML development, careful data curation, and the ability to quickly develop agricultural domain expertise
• Creative, naturally curious, and willing to take intellectual risks
• Adaptability to different business challenges and data types / sources and to learn and utilize a range of different analytical tools and methodologies
• Analytical problem-solving skills with innovative thinking, while effectively collaborating across diverse teams and managing multiple priorities in a…