Job Description

Nuseed delivers VALUE BEYOND YIELD ® through dedicated service, locally provencanola, carinata, sorghum, energy cane and sunflower seed for farm customers andnew plant-based solutions for end-use customers globally. This is coreto our global commitment and what sets us apart. Over 400 Nuseed employees work across our global locations, including three world-class Nuseed Innovation Centers. Established in Australia in 2006, Nuseed has grown to offer industry leading germplasm, advanced molecular capabilities, regional R&D and commercial teams in Australia, Europe, North America and South America, and sales in more than 30 countries. Nuseed is a wholly owned subsidiary of Nufarm Limited (AUX: NUF).

Key Purpose

Reporting to the Chief Technology Lead, and based in our West Sacramento Innovation Center, the data science leader will establish a data science team in Nufarm and lead a team of data scientists using computational approaches and technologies in support of discovery and product development across R&D and particularly for the breeding, breeding technologies, biotechnology and bioprocessing programs/projects. The selected leader is expected to design, coordinate and implement novel and comprehensive state of the art computational approaches (i.e. Algorithmic -based discovery, Artificial Intelligence and Machine Learning) using vast amounts of proprietary data in combination with other available information. The leader will work in close collaboration with Nufarm IT leads to seek support and coordination of the development of the required data engineering and hardware infrastructure. It is expected the selected candidate to have business acumen to develop financial plans for his/her team and business cases to support growth initiatives. It is also expected the data science leader will develop a good understanding of Nuseed’s products and technologies and develop new concepts to further improvement in collaboration with technical and business leads and scientists. This position will collaborate with the IT innovation team integrating software tools and databases that enhance data accessibility, analysis and reporting for R&D. The position is also a key contributor to the R&D overall strategy and is expected to accelerate delivery in the breeding and biotechnology pipelines (discovery to commercialization) to meet commercial objectives of the core seed, nutritional and bioenergy businesses.

Key Accountability

• Data Science strategy development, implementation, and delivery
• Develop a Nuseed data science strategy with an ultimate focus of developing tools for predictive breeding, output and agronomic trait discovery and metabolic engineering.
• Develop computational tools that are user friendly and enable scientists for routine discovery and development activities in R&D with focus on:
• Use diverse and comprehensive data for advancement decisions in the breeding program for the improvement of varieties and hybrids in the canola, carinata, camelina, energy cane, sunflower and sorghum breeding programs
• In silico identification and testing of ideas for native and biotechnology gene/trait discovery
• Computational modeling in pathway mapping, flux analysis and assessment of up/down gene regulation
• Data mining ranging from text mining to re-analyze historical data
• Develop standards for data collection, preservation of integrity and accessibility as well as evaluation of historical data sets to assess their value for data mining.
• Assess value of data available and new data needs to identify and map all the different data streams for strategy focus areas.
• Develop statistical and analytical models for field, environment and omics data that enable decision making.
• Partner with IT Nufarm team to develop the data engineering infrastructure required for building a data science platform and deliver the strategy.
• Partner with IT Nufarm team to ensure connectivity between LIMS, and breeding databases.
• Development of workflows to integrate laboratory management solutions with predictive analysis tools.

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