Data Science Manager

Job Brief:

The role is aimed at the ideation, qualification, prototyping, deploying and maintaining various data science solutions for the commercial team and wider organization. This would include more traditional, predictive models tackling areas such as churn and propensity to x modeling, segmentation, credit risk and customer scoring, coupled with some complex data manipulation and reporting ad hocs. As well as supporting or creating the more recent LLMs/ GenAI based solutions and applications.

Key Responsibilities:

• Propose, prototype and deliver to the consumer analytics and data science needs and roadmap.
• Prototype and build qualified models end-to-end, covering the full cycle from building specs and frameworks, to all modelling related aspects, full owning the final “packaging” and adoption.
• Manage existing predictive models, including churn and propensity ones. Through timely, periodical runs, and ensuring the same performance levels are maintained/ enhanced through features additions, re-trainings and all experimentation with different algorithms.
• Oversee, support and accept all external parties' engagements, including solutions vendors and consultancy engagements including solutions qualifications and specifications, data science aspects, delivery plans, UATs, with ongoing final users and leadership updates and alignment.
• Advising, structuring and delivering where required to the evolving commercial segmentation needs, serving the various areas from ecommerce personalization, to base management varying applications and credit risk and classification.
• Optimize all solutions in place, potentially to include modularization, code enhancements, MLOps compliance and deployment pipeline management, and platforms migration.
• Establish and maintain the operational rigor and project management best practices for the assigned areas and owned projects.
• Serve as the commercial point of contact in various enterprise committees and forums, including AI steering committees, data privacy and mega data projects.
• Support and ensure the commercial compliance to various data privacy regulations and guidelines, along with ensuring the ethical practices, transparency and explainability of the solutions in place.
• Staying abreast of major development in the space and within the telco domain, with a continuous quest to scout, identify and explore potential high value applications.
• Serve as a reference point and authority on all things artificial intelligence data science for the wider teams.

Leadership/management:

• Manage subordinates’ learning & development requirements and ensure that it is successfully delivered and provide related feedback to the management.
• Assign roles and work load distribution, set team objectives and conduct regular informal one-on-one meetings to deliver personalized feedback and provide support to attain goals.
• Conduct formal performance evaluation process for the subordinates in order to optimize workplace productivity
• Participate in the recruitment, selection, evaluation process of new employees.
• Provide guidance and delegate responsibilities to subordinates to facilitate performing different tasks to support succession planning process.
• Manage subordinates' administrational requirements.
• Assure that all subordinates are following Umniah’s policies and procedures and highlight any violation for the code of conduct.
• Manage economic, efficient and effective budget plan and control in alignment with the department and Umniah’s strategy.
• Ensure that employees are engaged and motivated through creating a respectful and trusting relationship with them and communicating Umniah values.
• Demonstrate flexibility in management by accommodating individual employee needs such as adjustable schedules or remote work options.

Requirements

Education:

• Bachelor's degree in artificial intelligence, computer science or software engineering related field. Or in a Business-related field, with an extensive numerical and computational component to it.
• Master's degree in data science or analytics field is a plus.

Level of Experience:

Advanced Experience in a pure-play data science domain, corporate analytics function, data warehouse/ data engineering areas, solutions vendors or analytics consulting house, with leadership capacities. With previous production-level implementations of predictive models and analytics solutions, having end to end frameworks around.

Technical Skills & Knowledge:

Essential:

• Predictive modeling using various algorithms families through Python and SAS (advantages).
• Familiarity with neural nets and deep learning-based modeling and applications.
• Familiarity with LLMs based applications and open-source model building processes.
• Deep knowledge in databases and distributed databases architecture and concepts.
• Data processing and preparation, comfortable with no-code tools or script based.
• Extensive experience in data tabulation, summarization and visualization.

Desirable:

• Social network analysis and graphs analytics at scale