Role Description Supporting training department by performing work educating, testing, and coaching new hires to a specified account using curriculum and materials specialized to a particular account. - Conduct new hire training in a classroom setting - Conduct follow-up one-on-one training when necessary - Conduct refresher training sessions - Conduct certification sessions - Attend training meetings - Attend training/quality assurance meetings - Assist training department with any other necessary functions - Conducts phone time when not facilitating a class or assisting with other training duties. Scorecard performance will be monitored. Qualifications - Healthcare Experience Preferred Company Description At Alorica, we only do one thing – make lives better, one interaction at a time. We’re a global leader in customer service and experience, serving the world’s biggest brands with tens of thousands of employees in hundreds of locations around the globe. - Largest minority-owned CX provider - Delivering digital-first, people-centered experiences for some of the world's most respected brands - Helping businesses solve their toughest customer experience challenges for over 25 years - Top Place to Work, serious about creating an environment where people show up as their genuine selves

• Design, build, and maintain scalable machine learning models that improve and automate logistics processes for our customers. • Own projects end-to-end, from problem definition and data exploration to model deployment and monitoring in production. • Collaborate closely with engineering teams to align ML work with customer needs and deliver features that drive business value. • Serve as a technical leader and mentor within the ML area, reviewing code and ensuring best practices for reproducibility, quality, and performance.

Role Description You'll play a key role by: - This role sits as part of a multidisciplinary squad, collaborating with other Machine Learning Scientists, Data Scientists, Backend Engineers, Operations specialists, Product managers, and Risk managers. - Leveraging your deep experience of developing and deploying advanced Machine Learning models to: - Automatically and accurately detect suspicious user behaviours while minimising impact to genuine customers and operational costs. - Adapt quickly and appropriately to changing fraud and financial crime trends, ensuring our detection systems remain performant through time. - Design machine learning solutions that scale globally. - The technical approaches you take to solve these problems will be very much in your hands and we’ll strongly encourage and support experimentation and innovation. - Justifying and demonstrating effectiveness along the way, ensuring the approach meets our business and customer needs. Qualifications - What we’re doing here at Monzo excites you! - You have a track record of deploying advanced Machine Learning models tackling real business problems with demonstrable impact, preferably in a fast moving tech company. - You're impact driven and excited to own the end to end journey that starts with a business problem and ends with your solution having a measurable impact in production. - You have a passion for sharing knowledge and raising the technical bar across the team. - You have a self-starter mindset; you proactively identify the most impactful issues and opportunities and collaboratively tackle them without being told to do so. - Using advanced ML techniques to ensure Monzo’s customers money stays safe, even if their card, phone or account is compromised, sounds exciting to you. - You have extensive experience writing production Python code and a strong command of SQL. - You are comfortable using them every day, and keen to learn Go lang which is used in many of our backend microservices. - You have experience developing and shipping deep learning, graph-based, and/or sequence-based ML architectures to production and delivering business impact. - You thrive working on ambiguous problems and have a track record of helping your team and stakeholders resolve that ambiguity. - You have strong communication skills and are able to explain complex technical concepts to non-technical stakeholders. - You want to be involved in building a product that you and the people you know use every day, with a product mindset that prioritises customer outcomes and data-informed decisions. - You're excited about fast-moving developments in Machine Learning and can communicate those ideas to colleagues who are not familiar with the domain. - You’re adaptable, curious and enjoy learning new technologies and ideas. Requirements - Experience in supporting your team in shaping the ML strategy of your area. - Experience working with financial crime, operations and in regulated institutions. - Commercial experience writing critical production code and working with microservices. - Experience in evaluating ML models in live environments such as through A/B tests. Benefits - We’ll help you relocate to the UK. - We can sponsor your visa. - This role can be based in our London office, but we're open to distributed working within the UK (with ad hoc meetings in London). - We offer flexible working hours and trust you to work enough hours to do your job well, and at times that suit you and your team. - £1,000 learning budget each year to use on books, training courses and conferences. - We will set you up to work from home; all employees are given Macbooks and for fully remote workers we will provide extra support for your work-from-home setup. - Plus lots more!

• Design, develop, and deploy production-ready machine learning models with a strong focus on computer vision applications. • Build AI capabilities that identify, track, and analyse construction progress from visual data. • Research, prototype, and implement solutions using LLMs, multimodal AI models, and generative AI technologies. • Evaluate when to leverage commercial foundation models versus developing custom machine learning solutions. • Design, build, and maintain scalable ML pipelines covering data collection, labelling, training, evaluation, deployment, and monitoring. • Source, clean, curate, and prepare high-quality datasets for model training. • Develop and maintain MLOps infrastructure, including model versioning, CI/CD pipelines, deployment automation, and monitoring. • Collaborate with Software Engineers, Product Managers, and AI specialists to integrate machine learning capabilities into production applications. • Optimise model accuracy, inference performance, scalability, and reliability. • Stay current with emerging machine learning techniques, AI frameworks, and industry best practices.