Senior Data Scientist

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

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