| Job details |
| Data Science Team |
| Job Title: |
Professional Data Scientist |
| Grade: |
P1 |
| Supervisor: |
Director for Data Science Unit |
| Location: |
HQ |
| Working Mode: |
Hybrid |
| Purpose |
A Professional Data Scientist works under the supervision of the Principal Professional Data Scientist. The holder of this position will contribute to the design, development, and deployment of advanced analytical models, machine learning solutions, and use AI techniques to solve complex institutional challenges, improve compliance, and support evidence-based decision-making.
|
| Key duties and responsibilities |
- Execute and contribute to the implementation and delivery of data science projects under the guidance of the Principal Professional Data Scientist.
- Develop, test, and deploy machine learning models for tax compliance, risk assessment, fraud detection, and other analytical needs.
- Perform data preprocessing, cleaning, transformation, and feature engineering on large-scale datasets (e.g., EBM transactions, tax declarations, customs data).
- Collaborate with IT, Compliance, Risk Management, and Policy teams to integrate analytical solutions into operational workflows.
- Assist in building, monitoring, and maintaining ML pipelines and workflows, ensuring reproducibility, scalability, and reliability.
- Contribute to the deployment of ML solutions in production environments, including containerized services, cloud platforms, and APIs.
- Implement and maintain ML model tracking, versioning, and performance monitoring using tools like MLFlow, Weights & Biases, or KubeFlow.
- Follow best practices in coding, documentation, version control, and data governance standards.
- Stay current with AI/ML tools, techniques, and industry trends, and suggest potential improvements or innovations.
- Participate in ad hoc analyses, model audits, and testing activities as required.
- Support knowledge sharing and mentoring of junior data professionals or interns.
- Prepare and deliver clear reports, visualizations, and presentations that communicate key analytical insights to stakeholders and support data-driven decision-making.
|
| Required Academic Qualification |
| Â Â Â Preferred Qualifications |
- Master’s Degree in Data Science specialized in Data Science
- Master’s Degree in Artificial Intelligence / Machine Learning specialized in Data Science
- 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
|
| Â Â Â Relevant Qualifications |
- Bachelor’s Degree in Data Science 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
- Bachelor’s Degree in Computer Engineering specialized in Data Science
- Bachelor’s Degree in Computer Science specialized in Data Science
- Bachelor’s Degree in Data Mining specialized in Data Science
- Bachelor’s Degree in Statistics / Applied Mathematics specialized in Data Science
- Bachelor’s Degree in Economics specialized in Data Science
- Bachelor’s Degree in Information Technology / Information Systems specialized in Data Science
|
| Skill Type |
Required Skill |
Required Proficiency level |
| Collaboration & Integration |
Proven ability in API development, building microservices, and collaborating effectively within a team. |
medium |
| Communication Skills |
Strong communication and presentation skills, with the ability to clearly convey complex data insights to both technical and non-technical stakeholders. |
advanced |
| Data Governance & Security |
Knowledge of data privacy, compliance regulations, and ethical AI practices. |
medium |
| DATA SCIENCE |
Familiarity with Python libraries (SciPy, StatsModels) for Feature Engineering & Statistical Analysis. |
advanced |
| DATA SCIENCE |
Skills in deploying and monitoring machine learning models using tools such as MLFlow, Weights & Biases, Kubeflow, Docker, and Kubernetes. |
advanced |
| DATA SCIENCE |
Data preprocessing and management using Python (Pandas, NumPy), SQL, Big Data tools (Spark, Hive), etc. |
advanced |
| DATA SCIENCE |
Experience working with machine learning development tools and frameworks such as Scikit-learn, TensorFlow, PyTorch, and Keras. |
advanced |
| DATA SCIENCE |
Advanced knowledge in data visualization, dashboard design, and reporting using tools such as Matplotlib, Seaborn, Plotly, Dash, and Tableau. |
advanced |
| Innovation & Continuous Learning |
Ability to continuously learn and apply emerging data science trends, tools, and innovative techniques. |
advanced |
| Web Development |
Web development with flask, jango, php, FastAPI, JQuery, html, CSS, Bootstrap, Postman, JavaScripts, etc |
medium
|
| Required Competencies |
- Accountability
- Client/Citizen Focus
- Communication
- Integrity
- Professionalism
- Analytical skills
- Decision making
- Time management
- Problem solving
- Teamwork
- planning
- Risk management
- RRA Business Acumen
- Ability to maintain accurate records and reporting
- Flexibility and adaptability
- Technology awareness
- Commitment to continuous learning
|
| Required Experiences |
- 2 years experience in Data Science for a Bachelor’s Degree holders and 1 year for a Master’s Degree holders
|
