Data Science Project Manager - Surrey, Canada - CropVue Technologies Inc

Description

CropVue Technologies is a leading agricultural technology company specializing in crop and pest monitoring solutions.

Our innovative platform combines advanced daily imaging, automated AI recognition and counting, bud and fruit growth tracking, and degree day modeling to help farmers optimize their crop yields, reduce input costs, and promote sustainable farming practices.

We are currently seeking a highly skilled Data Scientist to join our dynamic team who is capable to manage and oversee agricultural projects related to pest monitoring and new products underway, work with customers to develop pilot projects, coordinate with cross-functional teams, analyze field data, monitor project progress, and support business development efforts.

Key Responsibilities:

• Manage and oversee agricultural projects focused on pest management in orchards, vineyards, and vegetable crops.
• Work with customers to develop pilot project plans, timelines, and support logistics, setup, and operation of CropVue equipment in the field
• Coordinate with crossfunctional teams, including agronomists, entomologists, and field technicians in various time zones around the world
• Review CropVue field data to ensure reliable, consistent, and quality data delivery and anaylsis by CropVue hardware and cloud services using Python analysis scripts and CropVue cloud and web services
• Monitor project progress, track key outcomes, and prepare regular reports for management, highlighting achievements, challenges, and recommendations.
• Provide technical expertise and guidance to the team, assisting in problemsolving and ensuring the successful execution of project deliverables.
• Support business development efforts by participating in client meetings, presenting project proposals, and showcasing the company's capabilities in crop and pest monitoring.

General Responsibilities:
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Data Quality Assurance: Ensure the accuracy and integrity of data collected by CropVue hardware and cloud services. Implement data validation procedures to clean and preprocess data before analysis.
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Statistical Analysis: Conduct rigorous statistical analysis on field data to identify trends, correlations, and anomalies. Provide actionable insights to farmers and agronomists for improved crop management strategies.
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Stakeholder Engagement: Collaborate with academic and industry partners to leverage domain expertise in entomology, agronomy, and data science. Foster relationships to stay up-to-date with the latest advancements in precision agriculture.
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Project Budgeting and Resource Allocation: Manage project budgets, allocate resources effectively, and ensure timely completion of projects within financial constraints.
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Documentation and Knowledge Sharing: Maintain comprehensive documentation of project activities, methodologies, and results. Share knowledge within the team to facilitate continuous improvement.


Education:

Bachelor's Degree (required) in Data Science and/or Computing Science


Experiences Required:

• Proven experience as a Project Manager, ideally in crossfunctional team situations based in various time zones around the world
• Strong knowledge of data science, Python programming, report generation, and problem solving using data analysis approaches
• Excellent organizational, communication, and leadership skills, with the ability to effectively manage teams and build collaborative relationships. Excellent English verbal and written skills.
• Proficiency in project management tools and software.
• Passion for sustainable agriculture and a drive to contribute to the advancement of crop management technologies.
• Flexibility to travel occasionally for projectrelated activities and client engagements.

Experiences of key value:
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Advanced Data Analysis: Experience in advanced data analysis using Python libraries (such as pandas, numpy, scikit-learn) and statistical techniques to extract meaningful insights from complex datasets.
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Machine Learning: Proficiency in machine learning concepts and algorithms. Experience in building predictive models, classification algorithms, and regression analysis.
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Remote Sensing and GIS: Familiarity with remote sensing techniques and Geographic Information Systems (GIS) to integrate spatial data for better decision-making in precision agriculture.
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Data Visualization: Ability to create clear and informative data visualizations using tools like Matplotlib, Seaborn, or Folium to effectively communicate findings to both technical and non-technical stakeholders.
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Cross-Functional Collaboration: Previous experience working in cross-functional and international teams, especially in agriculture or related fields. Strong interpersonal skills to bridge communication gaps across disciplines.
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Project Management Certification: Possession of a project management certification (e.g., PMP) or relevant training, showcasing expertise in managing projects from initiation to co