Summary
IBS Software is seeking a Data Scientist for its Dubai location. The role involves developing data science strategy, collaborating with business stakeholders, analyzing large datasets, and building, evaluating, and monitoring predictive models. The successful candidate will have 5–8 years of experience, including mandatory experience implementing enterprise-level data science solutions and strong traditional machine learning expertise. The posting lists Python, R, TensorFlow, PyTorch, SQL, AWS, SageMaker, Bedrock, and generative AI frameworks among the technical requirements.
The posting
As published on ibsplc.com. Pulled from the employer's site and shown as they wrote it.
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
data Science, Machine learning