Lead Data Science Officer

23 hours ago

Doha, Qatar Qatar Airways Full time

About the role

As Lead Data Science Officer, you will be responsible for modelling complex problems, discovering insights and identifying opportunities through the use of statistical, algorithmic, mining and visualization techniques, integrating and preparing large, varied massive datasets, architecting specialised database and computing environments and communicating the results to the business. Turn big data into critical information and knowledge that can be used to make sound organisational decisions and drive change.

Key Responsibilities:

- Lead the expansion and enrichment of the department's existing big data sources (e.g. CDW, MIS, C360, VOC, QLAS, MSD) to include third party sources of information (e.g. unstructured data, market profiles, market data, other airlines' data, other industry data) to ensure that the company is kept up-to-date with trends and competitive insights, and that the data sources capture a holistic view of the customers.
- Lead the discovery processes with stakeholders to identify business requirements and expected outcomes by modelling complex problems, discovering insights and identifying opportunities through the use of statistical, algorithmic, mining and visualisation techniques.
- Expected to discover information hidden in the vast amount and variety of big data sources (e.g. CDW, MIS, C360, VOC, QLAS, MSD, other unstructured data) and perform statistical analysis to produce business insights e.g. market trends, customer profile and segments, association analysis, predicted behavior) that will enable the company to make better business decisions.
- Lead the utilization of various machine learning-based tools and processes such as regression, clustering principle component analysis and others within the Commercial Department.
- Model and frame business scenarios that are meaningful and which impact critical business decisions and processes.
- Develop scores (results of a particular modelling or predictive analytics exercise which help profile, categorize, or analyse a data), calculated values, or models using advanced analytics, mathematics and statistics, or other machine-learning techniques which can include recommendations, classifications, testing procedures, and anomaly detections.
- Identify what data is available and relevant, including internal and external data sources, leveraging new data collection processes such as smart meters and geo location information or social media.
- Make strategic recommendations on data collection, integration and retention requirements incorporating business requirements and knowledge of best practices.
- Develop innovative and effective approaches to solve analytical problems and communicates the results and methodologies
- Validate findings/test hypotheses using an experimental and iterative approach e.g. scenario modelling Identify/create the appropriate algorithm to discover patterns.
- Define the validity of information, how long the information is meaningful and what other information it is related to.
- Lead the creation of various machine learning-based tools or processes in the company, such as recommendation en ines or automated lead scorin s terns to make customer value visible and actionable to the department.
- Develop analysis, scores, and regular reports using machine learning techniques or state-of-the-art data mining methodologies and statistical analysis to help the department in understanding its business and operationalizing its action plans for improvements (e.g. calculated scores, values, or models for recommendations, classifications, testing procedures, and anomaly detections).
- Analyse reports and scores on a regular basis, and produce insights and action plans (e.g. marketing campaigns, programme modifications, targeted promotions, and tactical actions) to deliver value and support in data-driven decision-making.
- Develop data collection procedures and improve analytical tools and processes including recommendation systems, classifications, testing procedures, early-warning/alert reports, and anomaly detection.
- Manage and implement various projects in the department from end to end (e.g. Centre of Excellence, Voice of the Customer, Master Data Management).
- Work with IT and the teams in managing the vendors and other partners for the data mining tools.
- Educate the organisation on new approaches such as testing, hypotheses and statistical validation of results. Help the organisation understand the principles and the math behind the process to drive organisational buy-in.
- Interpret and provide solutions to business requirements covering commercial and marketing issues such as optimization, ROI analysis, lifetime value calculation, predictive analytics.
- Supervise and train other employees in the area of data science.

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