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