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Our client is one of the world’s leading agriculture companies.
Their mission is to contribute to the secure feeding of the world while prioritizing environmental responsibility. Their objective is to enhance the sustainability, quality, and safety of agriculture through cutting-edge scientific expertise and inventive crop solutions. The technologies our client employs empower millions of farmers worldwide to optimize the utilization of scarce agricultural resources.
Responsibilities
- Develop data science solutions for forecasting Accounts Receivable and/or Account Payable, with a strong emphasis on accuracy and integrity
- Work with SageMaker to leverage its capabilities for model development
- Solve regression and time-series-related problem statements using various advanced analytics methodologies, including Linear Regression, Machine Learning, Deep Learning, clustering, classification, segmentation, and more
- Perform model development and validation to improve model accuracy
- Cleanse, filter, and refactor complex data from different sources, and analyze it to identify its potential for generating high-accuracy models
- Utilize Python for data analysis and modeling
- Apply different machine learning algorithms to solve complex business problems
- Conduct statistical analyses and advanced data management using Python
- Create data visualizations to communicate insights to non-technical audiences
Requirements
- 5+ years of experience in developing and delivering data science solutions
- Experience in forecasting Account Receivable and/or Account Payable is highly beneficial
- Strong knowledge of regression and time-series problem-solving using advanced analytics methodologies
- Expertise in Python for data analysis and modeling
- Experience in statistical analysis and programming with Python
- Ability to create data visualizations for non-technical audiences
Nice to have
- Experience with data science use cases within business finance, production, supply, or commercial space
- Ability to explain complex data and analysis in a concise and understandable manner
- Familiarity with corporate financial data
- Understanding of SAP ECCs
- Experience with common analytics technologies such as SAP Analytics Cloud, Qlik, Tableau, Sage Maker, Azure ML, Data Bricks, KNIME, Alteryx, SAP BW, Redshift, S3, SQL, and others
- Proficiency in Sage Maker
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