Data Scientist

This is a broad and challenging role that works on a wide variety of problems. To name a few:

• • Address geocoding using deep learning.
• • Incomplete address auto-completion using natural language processing
• • Automatic time slot prediction
• • Dynamic territory optimization
• • Delivery risk modeling
• • Route planning and optimization
• • Geospatial demand prediction and capacity planning
• • Profiling and predictive analytics for customers, consignees and shippers.

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:

• • Professional degree in Computer Science, Operational Research, Statistics, Applied Mathematics, Engineering or Natural Sciences.
• • 2-5 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 is a plus.
• • Publications in peer-reviewed journals is a plus.
• • Most importantly an Inquisitive mind and the ability to constantly and independently ask questions, find answers, and learn