Supervisor Data Science at Uganda Revenue Authority

Nakawa, Bugiri, Uganda -

Full Time

Start Date

Immediate

Expiry Date

10 May, 26

Salary

0.0

Posted On

09 Feb, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Data Science, Machine Learning, Predictive Analytics, Data Analysis, Statistical Analysis, Data Visualization, Stakeholder Engagement, Project Management, Data Governance, Data Quality, Python, R, SQL, TensorFlow, Coaching, Mentoring

Industry

Government Administration

Description

JOB ROLE/ PURPOSE: To lead the implementation of data science projects to leverage data-driven insights, predictive analytics, and machine learning models that align with URA’s IT strategy and business objectives, enabling enhanced decision-making, operational efficiency, and revenue optimization. PRINCIPLE ACCOUNTABILITIES 1. Lead the development and implementation of data science projects a) Collaborate with stakeholders to identify data science opportunities and ensure project alignment with business needs b) Plan and execute data science projects, defining scope, objectives, and deliverables in alignment with URA’s strategic priorities c) Supervise the design, development, and implementation of data science solutions to address operational challenges and improve efficiency. d) Ensure adherence to established methodologies, standards, and project timelines e) Stay updated on emerging data science trends and technologies to inform project strategies and enhance outcomes 2. Oversee the development and deployment of data models and algorithms a) Design and develop machine learning models, statistical analyses, and predictive algorithms to address business challenges. b) Oversee the testing and validation of data models to ensure accuracy, reliability, and scalability. c) Facilitate the integration of data models into URA’s IT systems for seamless operational deployment. d) Monitor model performance in production environments and implement updates or retraining as needed. e) Document model development processes, methodologies, and outcomes for knowledge sharing and compliance 3. Collaborate with stakeholders to drive adoption data science. a) Conduct workshops and presentations to communicate data science capabilities and potential benefits to stakeholders b) Liaise with IT teams to ensure data availability and infrastructure support for data science initiatives c) Gather and incorporate stakeholder feedback to refine data science solutions and ensure alignment with business goals d) Build relationships with external partners, such as research institutions or technology providers, to enhance data science capabilities 4. Ensure data quality and governance in data science initiatives a) Develop and enforce data quality standards for datasets used in data science projects to ensure accuracy and reliability. b) Implement data governance policies to ensure compliance with URA’s data security and privacy regulations. c) Collaborate with data engineering teams to maintain robust data pipelines and optimize data retrieval processes d) Collaborate with data architecture teams to ensure datasets are properly structured and accessible for analysis. e) Review and validate outputs such as reports, dashboards, and visualizations for clarity and impact. 5. Plan and manage the performance of the business unit and staff under supervision so as to improve productivity and efficiency. a) Develop and monitor a Business Unit Work plan b) Develop performance agreements with all the staff under supervision. c) Monitor staff performance and provide guidance and periodic feedback for performance improvement. d) Prepare and submit quarterly M&E report e) Develop and update risk register for the business unit f) Document and review the unit business processes g) Respond to audit queries regarding the business unit and update the Audit system PERSON SPECIFICATIONS Essential Requirements a) An Honor’s Bachelor’s Degree in either; Information Technology, Data Science, Computer Science, Computer Engineering, Statistics, Applied Mathematics, Physics or Engineering from a recognized academic institution. b) At least three (03) years of experience in either data analytics or a data science role attained in a reputable organization. c) An impeccable record of integrity Desirable Requirements a) Professional Certification (e.g., Microsoft Certified: Azure Data Scientist Associate, Google Data Analytics Professional) or Postgraduate qualification in either; Data Science, Business Intelligence, Machine Learning, or Artificial Intelligence. b) Proficiency with data science tools and programming languages, such as Python, R, SQL, TensorFlow and relevant data visualization software. c) Certification in Project Management (e.g., PMP, PRINCE2) or Data Management (e.g., CDMP). Knowledge a) Experience in implementing data science projects in enterprise or public sector environments. b) Strong skills in data analysis, modelling, and machine learning algorithm development. c) Expertise in data visualization and storytelling to communicate insights effectively. d) Experience in stakeholder engagement and translating business needs into technical solutions. e) Familiarity with data governance policies and analytics best practices. f) Strong attention to detail and commitment to maintaining high data quality standards g) Knowledge of IT policies and regulatory frameworks. SPECIAL SKILLS AND ATTRIBUTES The incumbent should be able to demonstrate; a) The ability to communicate with impact and lead a team. b) Strong attention to detail and commitment to maintaining high data quality standards c) The ability to be flexible and adaptable but firm on principles d) The ability to prepare analytical reports and make presentations e) The ability to develop others through coaching, mentoring and guiding the professional growth of staff under supervision f) Excellent Data Analysis and problem-solving skills g) Excellent Organizational Skills h) Highly adaptable and versatile skills i) Punctuality and quick decision-making skills j) Resilience and commitment to work k) Possession of investigation skills

Responsibilities

The Supervisor Data Science will lead the implementation of data science projects to enhance decision-making and operational efficiency. This includes overseeing the development of data models, collaborating with stakeholders, and ensuring data quality and governance.