Responsibilities
You will:
- Communicate with impact your findings and methodologies to stakeholders with a variety of backgrounds.
- Work with high resolution machine and agronomic data in the development and testing of predictive models.
- Develop and deliver production-ready machine learning approaches to yield insights and recommendations from precision agriculture data.
- Define, quantify, and analyze Key Performance Indicators that define successful customer outcomes.
- Work closely with the Data Engineering teams to ensure data is stored efficiently and can support the required analytics.
Qualifications
- Demonstrated competency in developing production-ready models in an Object-Oriented Prog language such as Python.
- Demonstrated competency in using data-access technologies such as SQL, Spark, Databricks, etc.
- Experience with Visualization tools such as Tableau, Kepler.gl, etc.
- Experience with Data Modeling techniques such as Normalization, data quality and coverage assessment, attribute analysis, performance management, etc.
- Experience building machine learning models such as Regression, supervised learning, unsupervised learning, probabilistic inference, natural language modeling, etc.
- Excellent communication skills. Able to effectively lead meetings, to document work for reproduction, to write persuasively, to communicate proof-of-concepts, and to effectively take notes.
What makes candidates stand-out
- Experience with Geospatial data search and analysis, geo-indexing techniques, vector and raster data structures.
- Experience with remote sensing, GIS tools, and satellite imagery analysis.
- Experience with CVML
- Experience with advanced AI techniques and tools.
- Examples of professional work such as publications, patents, a portfolio of relevant project-work, etc.
- Familiarity with Distributed Datasets
- Experienced with a variety of data structures such as time-series, geo-tagged, text, structured, and unstructured.
- Additional experience with other languages such as Java, JavaScript, Scala, etc.
- Experience with simulations such as Monte Carlo simulation, Gibbs sampling, etc.
- Experience with model validation, measuring model bias, measuring model drift, etc.
- Experience collaborating with stakeholders from disciplines such as Product, Sales, Finance, etc.
- Ability to communicate complex analytical insights in a manner which is clearly understandable by nontechnical audiences.