The Role
Design, build, deploy, and monitor enterprise-scale predictive and classification models. Drive data strategy, align analytics with business goals, collaborate with stakeholders, perform feature engineering, ensure data quality, optimize workflows, and apply traditional ML and GenAI frameworks on AWS (SageMaker/Bedrock) to deliver actionable insights and forecasts.
Summary Generated by Built In
Job Title Data Scientist Location (s) Dubai Years of Experience 5-8yrs Job Description • Experience of implementing data science based solutions at at enterprise level is mandatory.
• Python,R, TensorFlow, PyTorch, SQL, AWS Cloud Platform, SageMaker, BedRock, Gen AI Frameworks and models,
• Strong traditional Machine Learning experience mandatory,
• Preditive analytics, Classification/Probability algorithms
• Develop a comprehensive data science strategy for the organization by aligning data initiatives with business goals, identifying key data sources, establishing data governance and management practices, implementing advanced analytics and machine learning solutions, and fostering a data-driven culture to drive innovation and decision-making
• Collaborate with business stakeholders to define problem statements, set clear expected outcomes using data science and artificial intelligence, and identify actionable business benefits and opportunities
• Examine extensive datasets to identify significant trends, patterns, and correlations that can provide valuable insights and inform strategic decision-making.
• Apply statistical techniques, data visualization, and analytical tools to uncover underlying relationships and actionable information from complex and voluminous data
• Build and refine predictive models, implement machine learning algorithms, and assess their performance to provide actionable insights and forecasts.
• In model building and evaluation, engineer features and select relevant variables, develop and train predictive models, and then evaluate their performance using appropriate metrics
• Optimize data science workflows to enhance efficiency and continuously improve the accuracy of predictions by streamlining processes, adopting best practices, and iterating on model performance through regular updates and refinements. This involves automating tasks, improving data quality, and implementing advanced techniques to ensure precise and actionable outcomes
• Continuously monitor the performance of deployed models, identifying any degradation in accuracy or relevance and taking necessary steps to retrain or fine-tune them as needed.
• Conduct regular data quality assessments, implementing procedures and tools to ensure the accuracy, completeness, and consistency of data used for analysis and modeling.
• Foster a data-driven culture within the organization, continuously improving data science processes by adopting new technologies, exploring innovative methodologies, and refining models for better results.
• Experience of implementing data science based solutions at at enterprise level is mandatory.
• Python,R, TensorFlow, PyTorch, SQL, AWS Cloud Platform, SageMaker, BedRock, Gen AI Frameworks and models,
• Strong traditional Machine Learning experience mandatory,
• Preditive analytics, Classification/Probability algorithms
• Develop a comprehensive data science strategy for the organization by aligning data initiatives with business goals, identifying key data sources, establishing data governance and management practices, implementing advanced analytics and machine learning solutions, and fostering a data-driven culture to drive innovation and decision-making
• Collaborate with business stakeholders to define problem statements, set clear expected outcomes using data science and artificial intelligence, and identify actionable business benefits and opportunities
• Examine extensive datasets to identify significant trends, patterns, and correlations that can provide valuable insights and inform strategic decision-making.
• Apply statistical techniques, data visualization, and analytical tools to uncover underlying relationships and actionable information from complex and voluminous data
• Build and refine predictive models, implement machine learning algorithms, and assess their performance to provide actionable insights and forecasts.
• In model building and evaluation, engineer features and select relevant variables, develop and train predictive models, and then evaluate their performance using appropriate metrics
• Optimize data science workflows to enhance efficiency and continuously improve the accuracy of predictions by streamlining processes, adopting best practices, and iterating on model performance through regular updates and refinements. This involves automating tasks, improving data quality, and implementing advanced techniques to ensure precise and actionable outcomes
• Continuously monitor the performance of deployed models, identifying any degradation in accuracy or relevance and taking necessary steps to retrain or fine-tune them as needed.
• Conduct regular data quality assessments, implementing procedures and tools to ensure the accuracy, completeness, and consistency of data used for analysis and modeling.
• Foster a data-driven culture within the organization, continuously improving data science processes by adopting new technologies, exploring innovative methodologies, and refining models for better results.
Technical Qualifications
• Python,R, TensorFlow, PyTorch, SQL, AWS Cloud Platform, SageMaker, BedRock, Gen AI Frameworks and models,
• Strong traditional Machine Learning experience mandatory,
• Preditive analytics, Classification/Probability algorithms
• Develop a comprehensive data science strategy for the organization by aligning data initiatives with business goals, identifying key data sources, establishing data governance and management practices, implementing advanced analytics and machine learning solutions, and fostering a data-driven culture to drive innovation and decision-making
• Collaborate with business stakeholders to define problem statements, set clear expected outcomes using data science and artificial intelligence, and identify actionable business benefits and opportunities
• Examine extensive datasets to identify significant trends, patterns, and correlations that can provide valuable insights and inform strategic decision-making.
• Apply statistical techniques, data visualization, and analytical tools to uncover underlying relationships and actionable information from complex and voluminous data
• Build and refine predictive models, implement machine learning algorithms, and assess their performance to provide actionable insights and forecasts.
• In model building and evaluation, engineer features and select relevant variables, develop and train predictive models, and then evaluate their performance using appropriate metrics
• Optimize data science workflows to enhance efficiency and continuously improve the accuracy of predictions by streamlining processes, adopting best practices, and iterating on model performance through regular updates and refinements. This involves automating tasks, improving data quality, and implementing advanced techniques to ensure precise and actionable outcomes
• Continuously monitor the performance of deployed models, identifying any degradation in accuracy or relevance and taking necessary steps to retrain or fine-tune them as needed.
• Conduct regular data quality assessments, implementing procedures and tools to ensure the accuracy, completeness, and consistency of data used for analysis and modeling.
• Foster a data-driven culture within the organization, continuously improving data science processes by adopting new technologies, exploring innovative methodologies, and refining models for better results.
• Experience of implementing data science based solutions at at enterprise level is mandatory.
• Python,R, TensorFlow, PyTorch, SQL, AWS Cloud Platform, SageMaker, BedRock, Gen AI Frameworks and models,
• Strong traditional Machine Learning experience mandatory,
• Preditive analytics, Classification/Probability algorithms
• Develop a comprehensive data science strategy for the organization by aligning data initiatives with business goals, identifying key data sources, establishing data governance and management practices, implementing advanced analytics and machine learning solutions, and fostering a data-driven culture to drive innovation and decision-making
• Collaborate with business stakeholders to define problem statements, set clear expected outcomes using data science and artificial intelligence, and identify actionable business benefits and opportunities
• Examine extensive datasets to identify significant trends, patterns, and correlations that can provide valuable insights and inform strategic decision-making.
• Apply statistical techniques, data visualization, and analytical tools to uncover underlying relationships and actionable information from complex and voluminous data
• Build and refine predictive models, implement machine learning algorithms, and assess their performance to provide actionable insights and forecasts.
• In model building and evaluation, engineer features and select relevant variables, develop and train predictive models, and then evaluate their performance using appropriate metrics
• Optimize data science workflows to enhance efficiency and continuously improve the accuracy of predictions by streamlining processes, adopting best practices, and iterating on model performance through regular updates and refinements. This involves automating tasks, improving data quality, and implementing advanced techniques to ensure precise and actionable outcomes
• Continuously monitor the performance of deployed models, identifying any degradation in accuracy or relevance and taking necessary steps to retrain or fine-tune them as needed.
• Conduct regular data quality assessments, implementing procedures and tools to ensure the accuracy, completeness, and consistency of data used for analysis and modeling.
• Foster a data-driven culture within the organization, continuously improving data science processes by adopting new technologies, exploring innovative methodologies, and refining models for better results.
Technical Qualifications
data Science, Machine learning
Skills Required
- 5-8 years of experience in data science
- Experience implementing enterprise-level data science solutions
- Strong traditional machine learning experience
- Experience with predictive analytics and classification/probability algorithms
- Proficiency in Python
- Proficiency in R
- Experience with TensorFlow
- Experience with PyTorch
- Proficiency in SQL
- Experience with AWS Cloud Platform
- Experience with AWS SageMaker
- Experience with Bedrock or similar GenAI platform
- Experience with Generative AI frameworks and models
- Ability to develop data science strategy and governance
- Experience monitoring and maintaining deployed models and ensuring data quality
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The Company
What We Do
IBS Software is a leading SaaS solutions provider to the travel industry globally, managing mission-critical operations for customers in the aviation, tour & cruise, hospitality and energy resources industries. IBS Software's solutions for the aviation industry cover fleet and crew operations, aircraft maintenance, passenger services, loyalty programs, staff travel & air-cargo management. IBS Software also runs a real-time B2B and B2C distribution platform providing hotel room inventory, rates, and availability to a global network of hospitality companies and channels. For the tour and cruise industry, IBS provides a comprehensive customer-centric, digital platform that covers onshore, online and on-board solutions. The Consulting and Digital Transformation (CDx) business focuses on driving digital transformation initiatives of its customers, leveraging its domain knowledge, digital technologies and engineering excellence. IBS Software operates from 16 offices across the world.
