Machine Learning Specialist – IRIS Simulation Tool Development      

The Revenue Mobilisation, Investment and Trade (REMIT) is a 39-months (2021 – 2025) programme funded by the UK’s Foreign, Commonwealth and Development Office (FCDO) and is implemented by Adam Smith International (ASI). The programme provides technical assistance (TA) to Pakistan with a broader objective to implement reforms for strengthening macroeconomic stability and improving conditions for high and sustained growth, mutual prosperity, job creation and poverty reduction. The programme works towards supporting the Government of Pakistan, its relevant ministries, institutions, and departments to strengthen revenue mobilisation reforms/initiatives, address investment environment challenges, facilitate trade and drive competitiveness by reducing trade barriers, and improve the macroeconomic policy and its management.

REMIT aims to provide support to FBR in designing, developing, and implementing a machine learning-based simulation tool specifically tailored for FBR’s key IT platforms—IRIS and WeBOC. This state-of-the-art tool aims to create a practical and immersive training environment that allows for both comprehensive skills development and precise assessment of trainees. The objective is to arm new recruits with not just theoretical understanding but actionable knowledge and expertise, allowing them to hit the ground running as they enter the workforce. This proficiency in utilising FBR’s core IT systems will improve the operational effectiveness of individual employees and elevate the collective efficiency and adaptability of FBR as a whole.

Job Summary

The role involves leading the design, development, and implementation of a machine learning-based simulation tool specifically for the FBR's IRIS 2.0 platform. This includes architecture design, coding, integration, and the inclusion of realistic simulation features for an immersive training experience. Responsibilities also cover pilot testing, deployment, ongoing technical support, and the creation of comprehensive user guides and training manuals.

Duties and Responsibilities

• Develop a detailed architectural plan for the IRIS simulation tool, tailored to meet operational and training requirements of the FBR.
• Design and code realistic functional features of the IRIS 2.0 platform, ensuring the simulation tool aligns with actual application functionalities.
• Build and manage a database to support the simulation tool, focusing on data integrity and security.
• Lead pilot testing with selected participants, evaluate tool effectiveness, usability, and integrate feedback for continuous improvement.
• Provide technical assistance for the tool's operation and prepare detailed user guides and training materials.
• Regularly report project progress to FBR management and REMIT team.
• Collaborate with internal stakeholders within FBR to ensure policy integration and adherence across departments.

Qualifications and Experience:

• At least 5 years of experience in designing and implementing machine learning-based tools, with a successful track record in similar projects.
• Advanced proficiency in programming languages and frameworks relevant to simulation tool development. Expertise in machine learning algorithms and their practical applications.
• Knowledge of the FBR's IRIS platform is highly advantageous.
• Strong background in UX/UI design, with a focus on creating engaging and user-friendly interfaces will be preferred.
• Ability to conduct pilot tests, analyse user feedback, and apply findings to enhance tool development.
• Proven experience in producing detailed technical and user documentation.
• Experience in the full software development lifecycle, from conception to deployment.
• Excellent problem-solving, analytical, and project management skills.
• Familiarity with the public sector, especially in areas related to taxation and revenue management, is highly advantageous.
• Strong communication and team collaboration skills.

Reporting

The “Machine Learning Specialist” will report to REMIT Tax Lead.

Level of Effort (LOE)

The level of effort for this assignment is around 60 days across 4 months.