Senior Data Scientist

AgZen; Somerville (MA); On-site; USD 150,000 - 180,000; Full time

Benefits offered by this job

Early-employee equity

401(k) with employer matching

6 weeks PTO on day one

12 paid holidays

Medical, Dental and Vision insurance

Job summary

AgZen, a Somerville, MA-based precision agriculture company, seeks a Senior Data Scientist to uncover patterns from crop protection data gathered from RealCoverage units worldwide. You will design dashboards, reports, and a data-driven recommendation engine bridging customer success and measurement teams.

The role requires strong ML and statistics skills, collaboration across teams, and in-person work in Somerville with seasonal travel to support major manufacturing builds.

Qualifications

• MS or PhD in Computer Science, Engineering, Statistics, Mathematics, Physics, or related field (or BS with equivalent work experience).
• Strong SQL/PostgreSQL skills and ability to write efficient data queries at scale.
• Proficiency in Python for production-quality code and data analysis.
• Experience with exploratory data analysis and data visualization.
• Background in statistical modeling and analysis from sensor data.
• Hands-on experience with ML and deep learning libraries (NumPy, Pandas, scikit-learn, PyTorch or TensorFlow).
• Ability to curate data and learn agricultural domain knowledge quickly.

Responsibilities

• Perform historical data analysis of spray applications and identify patterns and key factors.
• Apply statistical, ML, forecasting, optimization, and experimentation methods to solve agricultural data challenges.
• Architect and operationalize ML solutions including RealCoverage Recommendation Engine.
• Build tooling and support non-technical domain experts in understanding perception pipeline performance.
• Drive data-centric ML model improvements to hit milestones.
• Define and implement scalable data quality measures across multimodal data labeling pipelines.
• Contribute to data ontology and class structure for perception models.
• Collaborate with software engineers, ML scientists, product specialists, and researchers to build data pipelines.
• Communicate findings clearly to internal partners and external collaborators.

Skills

SQL / PostgreSQL

Python

EDA & Data Visualization

Statistical Modeling

ML / Deep Learning

Data Curation

Agriculture domain knowledge

Communication & Collaboration

Analytical problem solving

Education

MS or PhD in CS/Engineering/Statistics/Math/Physics or related field

BS with equivalent work experience

Tools

NumPy

Pandas

scikit-learn

PyTorch

TensorFlow

Job description

About AgZen:

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, RealCoverage, 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.

About the Role

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 RealCoverage 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.

What You’ll Do

• 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 RealCoverage 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

What We’re Looking For

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 multicultural scientific environment

Preferred:

• Experience with the field of agriculture or related fields such as environmental or life sciences

• Knowledge of interfacial science, crop science and/or fluid dynamics

• Experience with data science based on real-world physical sensors data

• Experience with vision-based ML

• Prior experience in developing data-driven customer facing recommendation system

• Experience creating intuitive data visualization tools that make complex data approachable for non-technical users

• Prior experience in developing machine-learning models relevant to biological or crop protection outcomes

• Advanced scientific Python (NumPy, Pandas, scikit-learn) and hands-on experience with PyTorch and/or TensorFlow, including training and deploying neural networks

• Hands-on experience leveraging generative AI approaches for data exploration, model development, or research acceleration

What We Offer

• The opportunity to make an immediate and visible impact in a fast-growing company

• Early-employee equity

• 401(k) with employer matching at 6 months of employment

• 6 weeks of PTO on day one

• 12 paid holidays

• Medical, Dental and Vision insurance

The salary range for this position is $150,000 - $180,000 depending on skills and qualifications evaluated on a per candidate basis.