Job title: Data Scientist
- The Data Scientist is responsible for modeling business processes and problems and for discovering actionable insights through descriptive, predictive, and prescriptive analytics.
- The Data Scientist will use Statistical, Machine Learning, Deep Learning, Data Visualization, and other analytics and AI techniques to gain understanding of the business processes and problems, and develop analytics solutions.
- The Data Scientist will contribute to building and developing data infrastructure for company and its portfolio companies and will support data exploration, preparation, collection, integration, and operationalization of data architectures and pipelines.
- The Data Scientist will be a data and analytics evangelist and an expert, and will promote the use of data and analytics capabilities and benefits to leaders of company portfolio companies and educate them in leveraging these capabilities in achieving their business goals.
- The Data Scientist will support the leadership with insights gained from data analyses and analytics, management reports, and analyses for decision-making processes
- Understand the decision-making process, workflows, and business and information needs of the users in company portfolio companies.
- Translates business needs into analytics requirements to support decision processes and workflows with required information.
- Works with the users to identify data-driven ML/AI/BI business opportunities and investigate solutions.
- Prioritize, scope, and manage Data Science projects and develop the corresponding KPIs to ensure project tracking and progress.
Data Integration and Exploratory Data Analysis:
- Develop access to databases and other data sources for exploratory data analysis.
- Work with domain experts to understand the business mechanics that generates the data.
- Identify data pipelines for efficient and repeatable data science projects that may span multiple divisions within a company or multiple companies under the Organization.
- Use data analysis and visualization techniques for studying data sets and develop insights while working with the business users
- Generate hypotheses about the underlying mechanics of the business process and test the hypotheses using quantitative methods.
- Perform large-scale data exploration to identify hidden or unknown relationships between variables in datasets, and validate or invalidate the new or existing hypotheses.
- Implement ML and other AI techniques to perform regression, classification, prediction, etc. as appropriate. This includes setting up, trialing, and testing hypotheses till a solution is identified while ensuring that the domain knowledge is effectively used and the business users are involved.
- Perform model testing in a structured manner ensuring validation of biases/fairness in the model.
- Research and implement state-of-the-art techniques and tools in machine learning, deep learning, and artificial intelligence to ensure that systems created are efficient and effective.
- Ensure that the data sources have sufficient data while selecting a model for production
- Determine and ensure availability and feasibility of data and data infrastructure requirements that will be needed to train, evolve, and operationalize models and algorithms.
- Visualize information and develop reports on the results of data analysis using data visualization tools and develop dashboards where a BI/descriptive analytics solution is appropriate.
- Be ready to continue to change course if hypotheses during model development are not supported by data analysis while keeping the objectives of the initiative in perspective.
- Make sure that common biases including confirmation bias, loss aversion, and anchoring bias, are kept in check during model selection and development.
- Use judgement to form conclusions that may challenge existing and conventional judgement and established ideas and thought, and focus on the goals of the initiative to identify high-leverage intervention points and strategies.
- Seek to understand business needs and get results that have a clear, positive, and direct impact on business performance.
- Apply multiple strategies including social and data-driven methods to convince others to change their opinions or plans and ensure that proposals or arguments are supported by effective logic and a business case while relevant factors are comprehensively addressed.
- Consider the relative costs and benefits of potential actions to choose the most appropriate one for selection, and operationalization of the proposed model.
- Be ready to learn, re-learn, and unlearn the problems while working simultaneously on multiple business units and portfolio companies.
- Rapidly acquire new knowledge and learn new skills as needed.
Data Pipeline Operationalization:
- Work with Data Engineering and IT to evaluate, select, and implement analytics deployment.
- Develop and help integrate model performance assessment and validation tools, and continuous monitoring in the deployed solution.
- Collaborate with Data Engineering to establish best practices for analytics production pipelines.
- Train peers in company and portfolio companies on Data Science principles and techniques.
- Help inspire the organizations about the business potential of Artificial Intelligence and other Data Science techniques
- Develop network of Data Science enthusiasts and professionals in the Organization Universe
- Keep abreast of evolving tools, technologies, and skills through self-learning, conferences, publications, courses, local academia and meetups.
Educational and Professional Qualifications:
- A Master’s degree in Computer Science, Engineering, Data Science, Operations Research, Statistics, Applied Mathematics, or a related field. Education in equivalent areas when complemented by suitable experience will be considered.
Location: Ras al-Khaimah
Job date: Thu, 13 Jan 2022 08:45:08 GMT
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