DATA
SCIENTIST

Working as a Data Scientist is one of the most challenging things you can do in the business world. It’s not for everyone, but those with the right understanding and knowledge could benefit greatly. 

LOCATION

Santa Clara

EMPLOYMENY TYPE

Permanent

What You’ll Do

The primary responsibilities of this role, Data Scientist Remote Sensing, are to:
 

  • Provide technical contributions in a fast-paced team environment to accelerate our efforts on building an analytics-driven product pipeline;

  • Acquire, process, transform, and extract information from high resolution remote sensing imagery;

  • Perform independent statistical analysis, computer programming, predictive modeling and experimental design;

  • Create innovative insights from imagery and sensor data with a focus on large scale geo-temporal analyses, computer vision and remote sensing, feature extraction from imagery and time series data, crafting complex model architectures using embeddings and ML/DL techniques;

  • Leverage business acumen and basic understanding of plant or agricultural systems to help build the next generation of our R&D field testing network;

  • Build cross-functional relationships to collaboratively partner with the business and effectively network within the Data Science Community;

  • Use advanced mathematical models, machine learning algorithms, operations research techniques, and strong business acumen to deliver insight, recommendations and solutions;

  • Develop sustainable, consumable, accurate, and impactful reporting on model inputs, model outputs, observed outputs, business impact and key performance indicators;

  • Present compelling, validated stories to all levels of organization, including peers, senior management, and internal customers to drive both strategic and operational changes in business.

Required Qualifications:

  • Must have one of the following:

    • Bachelor’s degree with at least five years of experience;

    • Master’s degree with at least two years of experience;

    • PhD with strong educational preparation and some applied experience;

  • Educational preparation or applied experience in at least one of the following areas: Geographical Information Systems, Machine Learning, Electrical/Industrial Engineering, Operation Research, Biostatistics, Computational Biology, Applied Mathematics, Computer Science and/or other related quantitative discipline;

  • At least 3 years of experience using R or Python

  • Demonstrated intermediate proficiency in computational skills and level of experience building data models using R, Python or other statistical and/or mathematical programming packages, including computer vision algorithms and libraries;

  • Demonstrated basic understanding of software development best practices (including Version Control, Code Documentation & Review, Cloud Based Sequence Analysis, Database Management);

  • Strong proficiency in predictive modeling to include comprehension of theory, modeling/identification strategies and limitations and pitfalls;

  • Strong proficiency with geospatial and imagery data such as geophysical soil sensing, remote sensing, hyperspectral, multispectral imagery, open source geospatial technologies and large-scale cloud computing;

  • Experience in successful delivery of valuable analysis through application of domain knowledge; evidence of ability to strong business acumen;

  • Strong communication competencies to include presentations and delivery of complex quantitative analyses in a clear, concise and actionable manner to extended team and small groups of key stakeholders.