Req Number: 18237BR
We are seeking a Data Scientist to join the Supply Chain Control Tower of Procurement & Supply Chain Management.
The Supply Chain Control Tower is responsible for providing complete end-to-end visibility over the supply chain with analytical capabilities to help identify areas of improvements and provide early warning systems and advanced decision support tools.
The Data Scientists primary role is to help discover the information & trends hidden in vast amounts of data generated by supply chain systems which will help in making smarter decisions to optimize processes and take better operational and strategic decisions. The primary focus will be in applying data mining techniques, doing statistical analysis, and building high quality prediction systems integrated with our systems. This includes but not limited to: automate scoring using machine learning techniques, build recommendations systems wherever applicable.
As the successful candidate you will hold a Bachelor’s degree in computer science, machine learning, mathematics, or statistics from a recognized and approved program. An advanced degree is preferred.
You will have more than five years’ experience in machine learning, statistical modeling, data mining, and analytics techniques
Experience with R, Python, or other statistical/machine learning software
Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, etc. Excellence in at least one of these is highly desirable
Experience with data visualization tools, such as D3.js, GGplot, etc.
Proficiency in using query languages such as SQL, Hive, Pig
Ability to accurately determine cause and effect relationships
Ability to communicate technical concepts and solutions at a level appropriate for technical and non-technical audiences
Experience in developing machine-learning algorithms, statistical and mathematical optimization models, and simulation and visualization tools
Understanding of regression modeling, forecasting techniques, time series analysis, machine-learning concepts such as supervised and unsupervised learning, classification, random forest, etc.
Experience developing experimental and analytic plans for data modeling processes and baselines
Understanding of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
Good applied statistics skills, such as distributions, statistical testing, regression, etc.
Good scripting and programming skills
Good knowledge of SAP-BW S4Hana is desirable
Duties & Responsibilities:
You will be required to perform the following:
Selecting features, building and optimizing classifiers using machine learning techniques
Extending company’s data with third party sources of information when needed
Enhancing data collection procedures to include information that is relevant for building analytic systems
Processing, cleansing, and verifying the integrity of data used for analysis
Doing ad-hoc analysis and presenting results in a clear manner
Identify pain areas in the business and area for improvement
The Engineering & Project Management (E&PM) business line studies, plans and oversees the construction of the Company’s new facilities, including some of the biggest and most complex projects in the petroleum industry. Recently, Saudi Aramco completed the largest capital program in its history that included new or expanded oil, gas and petrochemical facilities, raising maximum sustainable crude oil production capacity to 12 million barrels per day and significantly increasing gas production and processing capacities. Among the recently completed projects was the largest crude oil increment in the history of the industry: Khurais, with a production capacity of 1.2 million barrels per day. More challenges lie ahead, with a slate of new or expanded oil, gas, refining and petrochemical projects in the works. E&PM also manages the Company’s Research & Development Center where scientists investigate topics such as the desulfurization of crude oil, advanced fuel formulations for next generation combustion engines, and reservoir nano-scale robots (Resbots™) for injection into reservoirs to record their properties.