| Data Science Team |
| Job Title: |
Principal Professional Data Scientist |
| Grade: |
P3 |
| Supervisor: |
Director for Data Science Unit |
| Location: |
HQ |
| Working Mode: |
Hybrid |
| Purpose |
| The Principal Professional Data Scientist provides technical leadership, oversees advanced analytics projects, and guides a team of data scientists in delivering impactful solutions aligned with the institution’s strategic goals. The holder of this position is responsible for leading, mentoring, and coordinating the work of the Data Science team. The role ensures the design, development, and deployment of advanced analytical models by using machine learning (ML) and artificial intelligence (AI) techniques to address institutional challenges, improve compliance and service delivery, and to support policy formulation. |
| Key duties and responsibilities |
- Lead and manage the data science team, ensuring effective planning, task allocation, and delivery of high-quality results.
- Provide technical direction, mentorship, and capacity building to strengthen team skills and performance.
- Align data science projects with RRA’s strategic priorities and compliance objectives.
- Collaborate with IT, Compliance, Risk Management, Auditors, Enforcement, and Policy units to integrate solutions into tax administration.
- Translate business requirements into actionable data science projects and communicate findings through clear reports, dashboards, and presentations.
- Oversee the design, development, deployment, and lifecycle management of machine learning models and platforms.
- Direct complex analyses of large-scale datasets (e.g., EBM transactions, tax declarations, customs data) to generate actionable insights.
- Work with Data Engineering to design and maintain data pipelines supporting AI/ML models.
- Support ad hoc analysis and timely reporting to inform decision-making.
- Ensure all AI/ML solutions comply with governance, cybersecurity, privacy, and regulatory requirements.
- Promote adherence to best practices, ethical standards, and documentation in data science.
- Manage integration of external/third-party data sources in line with RRA’s governance frameworks.
- Stay updated on industry trends and emerging technologies to drive innovation in tax administration.
|
| Required Academic Qualification |
| Preferred Qualifications |
- Master’s Degree in Data Science specialized in data science
- Master’s Degree in Artificial Intelligence / Machine Learning specialized in datascience
- Master’s Degree in Big Data & Analytics specialized in data science
- Master’s Degree in Computer Engineering specialized in data science
- Master’s Degree in Computer Science specialized in data science
- Master’s Degree in Data Mining specialized in data science
- Master’s Degree in Statistics / Applied Mathematics specialized in data science
- Master’s Degree in Economics specialized in data science
- Master’s Degree in Information Technology / Information Systems specialized in data science
|
| Relevant Qualifications |
- Bachelor’s Degree in Data Mining specialized in data science
- Bachelor’s Degree in Computer Science specialized in datascience
- Bachelor’s Degree in Computer Engineering specialized in data science
- Bachelor’s Degree in Statistics / Applied Mathematics specialized in data science
- Bachelor’s Degree in Data Science specialized in data science
- Bachelor’s Degree in Information Technology / Information Systems specialized in data science
- Bachelor’s Degree in Economics specialized in data science
- Bachelor’s Degree in Artificial Intelligence / Machine Learning specialized in data science
- Bachelor’s Degree in Big Data & Analytics specialized in data science
|
| Skill Type |
Required Skill |
Required Proficiency level |
| DATA SCIENCE |
Proficiency in big data analytics tools and technologies, including MongoDB, Apache Hadoop, Apache Spark, Hadoop MapReduce, and related frameworks for processing and analyzing large-scale datasets. |
advanced |
| DATA SCIENCE |
Proficiency with machine learning frameworks and libraries, such as TensorFlow, PyTorch, Keras, scikit-learn, XGBoost, and LightGBM, for developing, training, and deploying predictive models. |
advanced |
| DATA SCIENCE |
Proficiency in AI, Machine Learning, NLP, and Deep Learning, with the ability to design, implement, and optimize intelligent solutions that address business challenges and support data-driven decision-making. |
advanced |
| Programming |
Proficiency in testing frameworks (pytest, unittest), code formatting and linting tools (black, isort, flake8), interactive coding and notebooks (Jupyter, IPython), API interaction (requests, aiohttp), and web scraping/parsing libraries (BeautifulSou |
advanced |
| Programming |
Experience with DevOps practices, including CI/CD pipelines, automation, containerization, and deployment of AI/ML solutions in production environments. |
advanced |
| Programming |
Experience in Python with strong knowledge of libraries such as NumPy, pandas, scipy and familiarity with software development best practices including version control (Git), testing, and packaging. |
advanced |
| Web Development |
Data visualization and development of analytical and operational dashboards |
advanced |
| Web Development |
Web development with flask, jango, php, FastAPI, JQuery, html, CSS, Bootstrap, Postman, JavaScripts, etc |
advanced |
| Required Competencies |
- Accountability
- Inclusiveness
- Integrity
- Leadership and supervision
- Professionalism
- Analytical skills
- Mentoring and coaching
- People, resources, time and performance management
- Problem solving
- Teamwork
- Planning, organization and coordination
- Details oriented
- Technology awareness
- Commitment to continuous learning
|
| Required Experiences |
- 6 years experience in data science for a bachelor’s degree and 3 years experience for a master’s degree.
|
