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 data scientists, data engineers, solution architects, application developers, and product managers. We are committed to enabling 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 Data Science Engineers in IDEAS are the brain behind every data-driven project/product. This role relies on a combination of analytics and technical expertise to turn the data vision into reality. As a Data Science Engineer, you are responsible for designing, building, and operationalizing large-scale predictive and prescriptive analytics to solve business problems that are important to Infor customers. You will solve complex and interesting business problems in demand forecasting, price recommendation, network optimization, production planning, inventory optimization, labor scheduling, anomaly detection, predictive maintenance, industrial IoT, computer vision, healthcare, location-based intelligence, human capital management, customer relationship management, etc. You will own the implementation of the reference architecture defined by the solution architect. You collaborate with the architect to finalize the solution blueprint and design decisions. The architecture design is then used as a steppingstone to implement the data pipeline and build and run scalable ML/AI models.

A successful candidate is expected to have an in-depth knowledge of the most common machine learning and optimization techniques and their applications. It is also expected that you keep up to date with evolving techniques in the field. The role also involves collaborating with business teams and customers to build data models that are faithful to the business and deliver solutions that realize business benefits. 

As a Data Science Engineer, you will have a holistic understanding of every component and feature being built, will play a central role in the team’s success, and will hold the key to deliver significant value to our customers. 

A Day in The Life Typically Includes:

• Develop, test, deploy, monitor, and improve/maintain machine learning and optimization models for enterprise applications.
• Work with product managers and Infor customers to understand business problems, identifying internal and external data requirements, and formulating data enrichment strategies for enterprise applications.
• Contribute to deploy and maintain Augmented Intelligence (AI) solutions using Infor Platform Technology for Infor customers to demonstrate the value of enterprise ML.
• Perform exploratory data analysis on large and complex datasets to assess data quality, extract, and share insights.
• Perform feature engineering, model selection, and model and performance tuning based on the available metrics. 
• Create, conduct, analyze, and interpret experiments and investigations.
• Design, implement and document scalable data models and clear data pipelines that reflect the business model and its constraints and process large data while keeping its integrity.
• Deliver high quality and reliable software successfully deployed to production in a timely manner.
• Communicate the results and their implications to the business stakeholders.
• Keep up with advancements in machine learning, deep learning, reinforcement learning, optimization techniques, and other advanced approaches.
• Keep current with new technologies to make continuous improvements.

Required skills:

• Engineering degree (or foreign equivalent) in Mathematical Modeling, Operations Research, Computer Science, Telecommunications, Electrical, Industrial or a related quantitative discipline from an accredited university.
• Proficient programming skills in Python, R, or Scala and data-querying skills in SQL and/or PySpark, etc. 
• Knowledge of machine learning algorithms with frameworks such as TensorFlow, PyTorch, or Keras. 
• Ability to communicate your ideas/code clearly through blog posts, kernels, GitHub. 
• Effective verbal and written communication, and presentation skills to explain difficult technical topics to different audiences from scientists to engineers to business partners. 
• Exposure to building data-centric analytics solutions.
• Good data modeling and problem-solving skills.
• A strong collaborative mindset with great interpersonal skills to help solve complex business problems.

Preferred Qualifications:

·        Demonstrated real-world experience in designing and implementing full machine learning and optimization solution pipelines, from ETL to data pre-processing, to quantitative data analysis, to model prototyping, to model deployment and inference.  

·        Experience with one algebraic modeling language e.g., MOSEL, AIMMS, AMPL, or GAMS, etc., and one commercial optimization solver e.g., Gurobi, CPLEX, or Xpress, etc.   

·        A proven desire to continue learning new technologies and techniques. 

·        Hands-on experience with Deep Learning algorithms. 

·        Demonstrated real-world experience in building and orchestrating big data pipelines of structured and unstructured data sets. 

·        Hands-on experience with frameworks in the Big Data ecosystem. Spark, Delta Lake, and Neo4j are preferred.