Infor

Infor Decision Analytics and Science (IDEAS) team is responsible for setting the innovation strategy at Infor. We are a diverse, well-integrated, and customer-focused team of scientists, engineers, solution architects, application developers, and product managers. We have an ambitious vision to enable enterprise Machine Learning (ML) for all Infor customers across multiple industries and segments. Our suite of industry-specific Augmented Intelligence (AI) solutions in Asset, Forecast, Customer, Pricing, People, Operational, and Process Intelligence domains provide rapid and measurable value to enterprise customers.

The scientists in IDEAS provide high-quality solutions in ML for Infor’s customers. This role requires and expands your technical expertise in a wide variety of technologies to develop and own our ML vision. As a scientist, you will contribute to the development of highly automated, robust, reusable, explainable, and self-serviced AI/ML solutions that are rapidly deployed and managed at scale. You will rely on a combination of analytics and technical expertise to turn the data vision into reality.

The scientist's role isn’t only about building scientific models. It also involves collaborating with engineering, product management, application development, and our customers to deliver solutions that realize business benefits. As a scientist, you will play a central role in the team’s success and hold the key to delivering significant value to our customers.

• Designing, developing, testing, deploying, monitoring, and improving machine learning models for enterprise applications.
• Working with product managers and Infor customers to understand business problems, identifying internal and external data requirements, and formulating data enrichment strategies for enterprise applications.
• Crafting, conducting, analyzing, and interpreting experiments and investigations.
• New data parsing, data modeling and analysis, anomaly detection, dataset cleansing, trend, and general characteristics extraction. 
• Feature engineering, eliminating redundant variables, and dimensionality reduction.
• Developing Machine Learning (ML) models that can be reused across multiple clients/projects which have a specific purpose.
• Performing a literature review to determine the breadth of models available and those which will provide us with robust and excellent performance.
• Documenting, testing, releasing, and disseminating clearly to the wider group.
• Mentoring junior members of staff guiding them on the best approaches to take in ML, increasing their knowledge base, and reviewing their work.

·  Engineering Degree (or foreign equivalent) in Computer Science, Statistics, Operations Research, Mathematics, Physics, or a related quantitative discipline from an accredited university.

• A strong background in statistics and computer science.
• Demonstrated real-world experience in designing and implementing a full ML solution pipeline, from ETL to data pre-processing, quantitative data analysis, model prototyping, model deployment, and inference.
• Strong programming skills in Python, R/matlab, and PyTorch.
• Working knowledge of SQL and PySpark.
• Effective verbal/written communication, and technical presentation skills. Ability to naturally explain difficult technical topics to everyone from data scientists to engineers to business partners.
• The ability to independently learn new techniques and stay up to date with developments in ML is essential.

What Will Put You Ahead?

• Ph.D. in quantitative discipline from an accredited university.
• Experience in developing real-world ML models for time series, clustering, classification, regression, etc. using statistical techniques.
• A broad rigorous knowledge of statistical techniques.