Brief Description of position:
Since its foundation in 1982, Aramex has grown to become a world leader in comprehensive logistics and transportation solutions recognized for its customized services and innovative products for businesses and consumers. Listed on the Dubai Financial Market (DFM) and headquartered in the UAE, we currently have business operations in over 567 cities across 66 countries worldwide and employ over 17,000 transportation professionals. Our breadth of services, including express courier, freight, logistics, supply chain management, e-Commerce and record management also give us considerable reach. We remain committed to further enhancing our global operations and pursuing more opportunities for business growth.
We live in an era where technology transforms and influences our daily lives more than ever before; therefore, technological innovation is critical to our success. We are strategically leveraging technology in a variety of ways and one of them is digital transformation by leveraging big data and AI.
In this position, your role will be to model complex problems and discover insights, through data mining, statistical analysis, machine learning and data visualization techniques and using state of the art big data & AI technologies.
This is a broad and challenging role that works on a wide variety of problems. To name a few:
- Geospatial and graph analysis, for optimization of ground and air networks
- Natural language processing of shipment information, customer addresses and customs clearance data
- Geospatial demand prediction and capacity planning
- Multi Vehicle Route Optimization with time and capacity constraints
- Classification, clustering and segmentation of customers based on their needs
The role will cover the following areas of responsibility:
- Consult with business users and stakeholders to identify relevant business issues and opportunities
- Identify available and relevant data on the problem, design and develop strategic methods for data collection, integration and retention.
- Integrate and prepare sets of large and varied data, organizing the data into a format that can be easily analyzed and managed
- Collaborate with subject matter experts, data managers and business analysts to better understand the meaning of data elements, origins, lineage and structures.
- Create and evaluate data models using statistical, algorithmic, data mining, visualization, and data matching techniques for data discovery and analysis.
- Present and summarize analysis results and recommendations to business users and stakeholders in an understandable and relevant way, verbally and visually.
- Document and publish analyzes, instructions and code so others can replicate and learn from previous work.
- Educate the organization about approaches and analytical principles in order to build support for the organization in the application of science data and advanced analytics.
- Lead or participate in development, test and delivery projects for analytical solutions
- Prepare and present progress reports and results to stakeholders.
To be successful in this role, you will need:
- PhD/M.Tech/M.S in Computer Science, Operational Research, Statistics, Applied Mathematics with a very clear understanding of probability and statistics, analytical approach to problem solving, and capability to think critically on a diverse array of problems.
- Publications in peer-reviewed journals will count in your favor.
- 3-5 years practical, applied data science experience in any industry
- Demonstrable hands on skills in manipulating large datasets, using databases, code-writing, data modeling, visualization, using one or more leading platforms and statistical languages (e.g. R, Python, Scala, Elastic, Hadoop, Spark, Tensorflow, AWS)
- Strong knowledge of, and the ability to explain statistical and mathematical concepts and techniques for quantitative analysis
- Supervised Machine Learning Algorithms like Logistic Regression, Bayesian Approach, Decision Trees, Support Vector Machines. Neural Networks, Ensemble Methods, Feature selection techniques etc. Understanding of advanced algorithms (i.e. Deep Learning) will be good to have.
- Good understanding of Unsupervised and Semi-supervised Machine Learning Algorithms
- Knowledge about optimization algorithms like Mathematical Programming (Linear/Non-linear techniques), Convex Optimization, Transportation Problem, Vehicle Routing Problem Simulated Annealing etc would be beneficial
Most importantly an Inquisitive mind and the ability to constantly and independently ask questions, find answers, and learn