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Data Scientist

Full time at P&O Maritime Logistics in UAE
Posted on April 14, 2021

Job details


Based at our Head Office in Dubai and reporting to the Head of IT - POML, the Data Scientist   will play a pivotal role in planning, executing and delivering machine learning-based projects. The bulk of the work will be in machine learning (ML) modelling, management and problem analysis, data exploration and preparation, data collection and integration. The role must also cover the related roles of data engineer and DevOps engineer at times.




Problem Analysis and Project Management

  • Guide and inspire the organization about the business potential and strategy of artificial intelligence (AI)/data science
  • Identify additional data sources for revenue generation opportunities
  • Identify data-driven/ML business opportunities
  • Collaborate across the business to understand IT and business constraints
  • Prioritize, scope and manage data science projects and the corresponding key performance indicators (KPIs) for success

Data Collection and Integration

•             Understand new data sources and process pipelines, and catalogue/document them

•             Acquire access to various databases, and other source systems such as SQL or graph databases

Data Exploration and Preparation

  • Apply statistical analysis and visualization techniques to various data, such as hierarchical clustering, T-distributed Stochastic Neighbour Embedding (t-SNE), principal components analysis (PCA)
  • Generate hypotheses about the underlying mechanics of the business process
  • Test hypotheses using various quantitative methods
  • Display drive and curiosity to understand the business process to its core

Machine Learning

  • Apply various ML and advanced analytics techniques to perform classification or prediction tasks
  • Integrate domain knowledge into the ML solution; for example, from an understanding of financial risk, customer journey, quality prediction, sales, marketing


  • Collaborate with DB teams to  evaluate and implement ML deployment options
  • Integrate model performance management tools into the current business infrastructure
  • Implement champion/challenger test (A/B tests) on production systems
  • Continuously monitor execution and health of production ML models
  • Establish best practices around ML production infrastructure

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