Job Description

Problem Analysis and Project Management:

- Guide and inspire the organization about the business potential and strategy of artificial intelligence (AI)/data science.

- Ability to define a research problem into a data science usecase.

- Translate production problems into data-driven analytics / machine-learning tasks and building related products which automate the operational functions in production and manufacturing areas.

- Ability to understand agronomy research data and the opportunity to combine that with weather and other public data sources to generate insights for researcher and production teams.

Data Collection and Integration

- Design strategies and implement algorithms to analyze and leverage data; assessing the effectiveness and accuracy of data sources to be used as inputs in data analysis; to implement visualization systems that make data readily available and simplify how insights are communicated.

- Acquire access to various databases, and other source systems such as SQL and/or graph databases.

- Design and create data pipelines for more efficient and repeatable data science projects.

- Collaborate with cross-functional teams, including data engineers, software developers, and business stakeholders to ensure successful implementation of data science solutions.

Data Exploration and Preparation:

- Identify opportunities for new and existing weather models to create value in supply reliability; parent line production; crop management.

- Network with domain experts to better understand the business mechanics that generated the data.

- Knowledge and Capability to apply various techniques to boost project outcomes: e.g., transfer learning, synthetic data, federated machine learning,

- Create visualizations and dashboards to communicate insights effectively to both technical and non-technical stakeholders.

Machine Learning:

- Apply various ML and advanced analytics techniques to perform diverse classification or prediction tasks.

- Detection of crop response to sync management techniques, fertilizers, and other crop production inputs; development of crop and pest management aids based on aerial images from drones in large production fields; validation of small plot data collected by aerial sensors with established ground-based information.

- Building and supporting use cases such as NDVI image analysis, Planting predictions, germination prediction, plant growth model, weather model disease prediction etc.

- Integrate model into the current business process and infrastructure.

- Continuously monitor execution and health of production ML models

Skills and Expertise:

- Experience in building advanced analytics models across one or more areas of Predictive/Prescriptive Analytics, and Image Processing.

- Proven examples of teamwork. Proven examples of caring and respecting workforce. Proven examples of innovation.

- Candidates should have five years of relevant project experience in successfully executing data science projects.

- Work in cross-disciplinary project teams of database specialists, IT specialists, business subject-matter experts and agronomists who will bring the domain expertise to our data-driven solutions.

- Experience with data visualization tools such as Tableau or Power BI

- Excellent communication and presentation skills

- Candidates need to demonstrate that they were instrumental in executing end-to-end significant data science projects.

- Experience with analytical packages, SQL, Python, R, Spark, Statistical packages like Statistics, Minitab, Azure cloud, notebooks (data bricks/Jupyter) is a must

- Able to build data acquisition pipeline using python and comfortable in doing data engineer to acquire the data required for the model.

- Able to get public data set via API, data pipeline and combine that with internal company data for the EDA (Exploratory data analysis)/ ML etc.

Education and Experience:

- A bachelor\'s or master\'s degree in computer science, data science, operations research, statistics, applied mathematics, or a related quantitative field preferred. Alternate experience and education in equivalent areas such as economics, engineering, or physics, is acceptable. Experience in more than one area is strongly preferred.

- Masters in Statistics, Mathematics, Machine Learning, Agricultural Statistics or Climatology

- Experience in the agriculture industry building sales and marketing advanced analytics usecase would be a plus.

- Experience working on seed research data structures that have complexities around parent lines, crop cycle, weather, satellite images etc.

- Able to work on global data sets that are pulled from different public sources is a bonus.

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