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Responsibilities:

The data science engineer will be a critical part of a small team tasked with advanced analytics related to sustainable agriculture and large-scale crop operations. In this role, you will:

• Provide technical expertise to a team strong in agronomic sciences
• Leverage statistical analysis and machine learning to support informed, data-driven decision making
• Incrementally improve a data science framework over time leveraging sound software engineering principals, automation, and best practices in ML Ops
• Play a leading technical role on the team spanning the entire data science lifecycle
• Collaborate and communicate effectively across business functions to support data science initiatives
• Innovate, explore, and practice the art of the possible to uncover valuable data science opportunities and bring them to fruition

Requirements:

• Bachelor’s degree in data science, computer science, or related technical field
• 5+ years of experience in statistical analysis and machine learning within the Python ecosystem including TensorFlow, PyTorch, pandas, NumPy, and sklearn
• 3+ years of experience creating ML pipelines incorporating both structured and unstructured data
• 3+ years of experience working with various data sources including diverse file types, web service APIs, relational databases, and NoSQL
• 3+ years of experience implementing data science solutions on AWS, Google, or Azure cloud
• Experience with supervised and unsupervised learning techniques as well as classification, regression, and anomaly detection problems
• Broad understanding of machine learning including model training, hyperparameter tuning, optimization, performance evaluation, inference, model interpretability, and GPU acceleration
• Experience scaling and optimizing data workloads on distributed systems
• Experience working with big data file formats such as Parquet, Avro, and Iceberg
• Proven ability to perform development at all stages in the data science lifecycle
• Foundational understanding of software engineering, algorithms, and data structures
• Familiarity with model lifecycle management and ML Ops concepts
• History of continuous learning and incorporation of open data and software to solve problems
• A passion for software, data, and AI

Preferred qualifications:

• Master’s degree in data science or related technical field
• Experience with data science on Databricks including Apache Spark and MLflow
• Experience processing geospatial datasets and file formats
• Experience working with time series data and sequence processing
• Experience collaborating on cross-functional agile teams and presenting to key stakeholders
• Foundational understanding of computer vision, NLP, and generative AI
• Ability to interpret results, visualize output, and communicate data narratives to stakeholders
• Working knowledge of regenerative agriculture, crop operations, and soil, plant, or climate sciences.