ETIC, Data Science - Manager

Cairo - Eticmid

OtherData Science Manager

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Overview

Line of ServiceAdvisory Industry/SectorTechnology SpecialismAdvisory - Other Management LevelManager Job Description & SummaryAt PwC,

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OtherData Science Manager

Advisory

Technology

Advisory - Other

Manager

At PwC, our people in data and analytics engineering focus on leveraging advanced technologies and techniques to design and develop robust data solutions for clients. They play a crucial role in transforming raw data into actionable insights, enabling informed decision-making and driving business growth.

Those in data science and machine learning engineering at PwC will focus on leveraging advanced analytics and machine learning techniques to extract insights from large datasets and drive data-driven decision making. You will work on developing predictive models, conducting statistical analysis, and creating data visualisations to solve complex business problems.

We are seeking a highly skilled and experienced Data Scientist to join our team at a leading professional services firm. The ideal candidate will have a strong background in data science, analytics, and machine learning, with at least 4 years of relevant experience. This role involves working closely with cross-functional teams to derive actionable insights from complex data sets and support strategic decision-making about how we attract, develop, and retain top talent, improve decision-making about our people, and deliver business-driven, actionable insights.

Responsibilities

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→Design, develop, and implement advanced data models and algorithms to solve business problems.
→Analyze large and complex data sets to identify trends, patterns, and insights.
→Collaborate with stakeholders to understand business requirements and translate them into business questions and data solutions.
→Develop and maintain dashboards, reports, and visualizations to communicate findings effectively.
→Apply machine learning and AI techniques to enhance data-driven decision-making.
→Ensure data governance, quality, and compliance with internal and external standards.
→Stay current with industry trends and emerging technologies in data science and analytics.

Requirements

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Bachelor’s or Master’s degree in Data Science, Computer Science, Statistics, Mathematics, or a related field.
Minimum of 4 years of professional experience in data science or analytics roles.
Technical proficiency in complex statistical analysis using tools such as R or Python.
Experience with machine learning frameworks and libraries (e.g., scikit-learn, TensorFlow, PyTorch).
Knowledge of generative AI models and their applications.
Experience using APIs, SQL and other technical methods to extract and combine data from various sources.
Strong skills in data visualization tools such as Power BI.
Excellent analytical, problem-solving, and communication skills, both written and verbal (in English).
Flexibility to work across time zones and support global clients.

Advanced degree (MSc or PhD) in a quantitative field.
Experience in extracting and working with large and complex people data sets, preferably Workday HCM.
Experience working in a professional services or consulting environment.
Experience in managing analytics projects in a complex, matrixed environment.
Familiarity with cloud platforms such as AWS, Azure, or GCP.

Strong interpersonal and stakeholder management skills.
Ability to work independently and collaboratively in a team environment, providing coaching, feedback and supervision.
Excellent time management and organizational skills.
Eagerness to innovate and learn new technologies, failing fast and learning from mistakes.
High attention to detail.
Adaptability and resilience in a fast-paced environment

Nice to Have

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Accepting Feedback, Accepting Feedback, Active Listening, Analytical Thinking, Artificial Intelligence, Big Data, C++ Programming Language, Coaching and Feedback, Communication, Complex Data Analysis, Creativity, Data-Driven Decision Making (DIDM), Data Engineering, Data Lake, Data Mining, Data Modeling, Data Pipeline, Data Quality, Data Science, Data Science Algorithms, Data Science Troubleshooting, Data Science Workflows, Deep Learning, Embracing Change, Emotional Regulation {+ 23 more}