Debt Collector - Call Centre

Job Overview As a Principal AI Engineer within our Engineering team, you will be a key innovator, applying cutting-edge artificial intelligence and machine learning methodologies to augment and amplify the impact of our Data Science team.

You will collaborate closely with other Data Scientists and teams to create scalable, reliable AI / ML solutions that address complex challenges in health and policy.

Your work will focus on pushing the boundaries of what's possible with AI / ML, mentoring and training team members, and promoting ethical and responsible use of AI / ML in the organisation.

Key Focus Areas

1. Research & Development : Work closely with the Head of Engineering and Data Science Lead to align AI / ML strategies with broader organisational goals, while incorporating their strategic direction. Stay at the forefront of AI / ML advancements by continuously exploring emerging technologies and techniques. Identify and experiment with new AI / ML methodologies to address challenges specific to Reach's projects. Develop prototypes and proof-of-concept AI / ML models to demonstrate their potential value and application within the organisation.
2. Model Design & Implementation : Contribute to the design and development of high-impact AI / ML models that address critical organisational challenges and opportunities. Collaborate with other Data Scientists to choose appropriate architectures and algorithms for tasks like recommendations, predictive modeling, and natural language processing. Fine-tune and optimise models to ensure optimal performance on real-world datasets and prepare them for deployment in production environments. Ensure that AI / ML models are designed with ethical considerations in mind, incorporating fairness, transparency, and accountability from the outset.
3. Data Engineering & Model Deployment : Partner with Data Engineers to ensure access to clean, well-structured datasets ready for AI / ML model training. Preprocess and transform data to make it suitable for AI / ML model input, while actively monitoring data quality to identify and address any issues that could impact model performance. Contribute to the operationalisation of AI / ML models into production environments, ensuring their scalability, reliability, and smooth integration with existing systems. Contribute to the design and implementation of MLOps frameworks to automate deployment, monitoring, and management of AI / ML models across various environments.
4. Model Monitoring & Maintenance : Develop processes and guidelines on monitoring model performance over time, identifying any degradation or biases that may arise. Implement robust monitoring frameworks to track model performance in production, ensuring early detection of any issues. Develop and implement mechanisms for model retraining and updating as new data becomes available, ensuring models remain accurate and relevant. Lead the development of tools and systems to automate model maintenance, retraining, and scaling. Collaborate with other Data Scientists to interpret model results and derive actionable insights. Ensure that AI models are developed with an acute awareness of the potential risks and consequences of irresponsible use.
5. Collaboration & Communication : Mentor, train, and share knowledge with other Data Scientists, fostering a culture of continuous learning and innovation. Foster a strong, collaborative partnership with other teams, understanding their needs and priorities. Work closely with the Head of Engineering and Data Science Lead to make informed strategic decisions that guide AI / ML initiatives. Explain AI / ML concepts and results in clear, concise language to both technical and non-technical stakeholders, ensuring everyone understands the value and implications of AI-driven solutions. Present findings and recommendations to senior management and technical teams, advocating for the adoption and integration of AI into Reach's work. Draft and publish research and project findings, supported with knowledge transfer internally and externally.

Responsibilities and Duties

1. Design, develop, and deploy AI / ML models in collaboration with other Data Scientists and Data Engineers in areas such as: Predictive modelling for early identification of health and behaviour risks. Recommendations for delivering personalised content and suggestions to users. Natural language processing for analysing qualitative data. Anomaly detection for identifying patterns and trends in large datasets.
2. Contribute to the design and development of high-impact AI / ML models that address critical organisational challenges and opportunities.
3. Contribute to the end-to-end MLOps lifecycle, from model development to deployment, monitoring, and maintenance.
4. Provide strategic guidance on AI / ML best practices, ensuring models are built with scalability, security, and ethical considerations in mind.
5. Lead efforts in mentoring and developing the AI / ML capabilities of other Data Scientists, fostering a culture of continuous learning and improvement.
6. Champion the implementation of ethical AI practices, ensuring that models adhere to principles of fairness, accountability, and transparency.
7. Evaluate and mitigate potential risks associated with AI / ML models.
8. Monitor and evaluate the performance and impact of AI / ML models.
9. Provide training and support to other Data Scientists on the use and interpretation of AI / ML tools and results.
10. Stay abreast of the latest advancements in AI research and advocate for their responsible and ethical adoption.

Qualifications

1. Master's or Ph.D. in a relevant field (e.g., Computer Science, Artificial Intelligence, Machine Learning).
2. 5+ years of experience in applied AI research and development, with significant experience in MLOps and operationalising machine learning models in production environments.

Skills and Experience Required

1. Strong expertise in machine learning frameworks and programming languages (e.g., Python).
2. Deep experience with MLOps practices and tools, including CI / CD pipelines, model versioning, and automated deployment.
3. Proven track record of leading AI / ML projects from conception to production at scale.
4. Experience with a range of AI / ML techniques, including deep learning, reinforcement learning, natural language processing and transformer models.
5. Demonstrated experience in designing and implementing ethical AI practices, with a strong understanding of fairness, accountability, and transparency in AI.
6. Ability to evaluate the potential risks and consequences of AI / ML models, particularly in sensitive projects, and implement safeguards to mitigate these risks.
7. Demonstrated experience in mentoring, training, and knowledge sharing within technical teams.
8. Proven ability to collaborate effectively with cross-functional teams.
9. Excellent communication and presentation skills, both written and verbal.
10. Passion for social impact and a commitment to ethical AI development.