The Adaptive Intelligent Applications (AI Apps) team at Oracle Corporation is working to develop and deploy AI and Machine Learning based solutions at scale and throughout all of Oracle’s existing products and services. In addition, it builds new products in areas where AI and Machine Learning are essential to enable new business.
We are seeking to grow the AI Apps team with brilliant and diverse individuals with exceptional technical ability. This is a challenging role that will stretch your knowledge and curiosity, while at the same time is a great opportunity to learn new skills and work within an unusually talented, global community at Oracle.
You will encounter a wide variety of data types, from retail and financial transactions to free text, images and video. AI Apps are required to solve diverse business challenges ranging from recommendation systems and dynamic discounting, management of the flow of goods and services, transportation logistics and movement and storage of materials and inventory, accounting and procurement, project management, manufacturing, staff recruiting, managing and optimizing the HR of an organization.
This is a hands-on position where you will be empowered to be very ambitious and bold, to solve challenging problems and have the potential to directly impact Oracle’s future. The role requires that you have an extensive background in machine learning and data mining. A proven track record in inventing and modifying advanced innovative algorithms, and applying them to large data sets is essential. You will be a team player who is eager to both teach and learn on a daily basis, that is proactive and self-motivated and has excellent communication skills.
- Solve business and technical problems with robust and statistically sound use of rigorous scientific methodologies and creative use of algorithms using AI, machine (deep) learning and predictive modelling techniques.
- Complete end-to-end execution of the data science process. This may be carried out in a collaborative environment with product and engineering teams, but ranges from understanding business requirements, data discovery and extraction, model development and evaluation, to production pipeline implementation.
- Be comfortable and proactive in an environment that combines well defined problem specifications with at times unpredictable situations carrying a significant number of technically and functional unknown
- Make consistent use of solid verbal, interpersonal and written communication skills to carefully document results and findings and share results with various stakeholders.
- Have the will and competence to explore ways to collaborate across a number of teams and leverage solutions already being developed and technology that has been demonstrably effective elsewhere.
- Be confident and familiar with tools and styles to work remotely in effective ways together with a global team located in multiple geographical locations.
- Actively participate as contributor or leader in a team of peer data scientists, understanding the collaborative and transparent relationships with engineering and product teams and the ways of working of an agile environment.
Adaptive Intelligent Applications team qualifications
We are looking for ambitious scientists with an exceptional academic background, an ideal blend of coding, machine learning and statistics, a colleague with whom we can share the enjoyment of being curious, the interest in difficult mathematical and algorithmic problems, and the drive to be innovative in building predictive models as well as in the way society deals with sensitive data.
We would like you to have
- An advanced degree in Computer Science, Physics, Engineering, Mathematics, or another relevant quantitative field.
- Excellent understanding of the mathematical theory behind algorithms underlying common machine learning techniques for solving classification and regression problems in a supervised setting as well as approaches for unsupervised learning.
- Multi-years postgraduate experience in AI, machine learning, data mining, analytics and/or predictive modelling.
- Real-world practical experience with machine learning algorithms for classification, regression, clustering, reinforcement learning, dimensionality reduction with expertise one or more application domains of NLP, image processing, time series analysis.
- A proven track record in developing, innovating, and applying advanced algorithms to address practical problems and in building new analytical products of commercial value.
- Practical experience in feature engineering, feature evaluation, feature selection and automation of such tasks, model interpretation and visualization.
- Robust knowledge and experience with statistical methods, in particular with the estimation of confidence intervals around parameter values and predicted quantities.
- Domain expertise in one or more of online retail, digital marketing, financial services, insurance, health care, manufacturing, consumer goods, telecommunications.
- Proficiency with several years’ experience in more than one of Python, R, Java, C, C++, Scala, and robust Linux shell scripting.
- Proficiency in using query languages such as SQL and its adaptations.
- Experience with horizontally scalable data stores such as Hadoop and other NoSQL technologies such as Map Reduce, Spark, HBase, etc., and associated schemas.
- Experience with application agile and iterative development practices and version control systems.
It would be fantastic if you also have
A PhD degree in a quantitative Science or technical field.
Post-doctoral academic research experience in AI and Machine Learning
Demonstrated experience in engaging and influencing business leaders in solution path design.
Worked with GPUs and have used CUDA programming.
Experience with one or more of the DNN frameworks, including TensorFlow, MXNet, Theano, etc. and applications of such libraries to NLP problems.
Practical experience with deep learning techniques for text processing and modelling.
Expertise with text processing tasks such as automatic summarization, translation, named entity recognition, relationship extraction, sentiment analysis, speech recognition, topic segmentation
Experience in leading and mentoring other data scientists.