Machine Learning Engineer - Intelligence Group

About this role

Fraud is a huge problem affecting millions of people, it costs the UK nearly £7bn and represents 40% of all crime. Too often the solution has been to put in place cumbersome authentication processes that frustrate genuine customers, cause inefficiencies for organisations and fail to prevent fraud. We are changing this by providing organisations with real-time insight into the risk of a caller. We combine patented machine learning technology with our deep domain knowledge to prevent contact centre fraud and streamline customer experience. We recognise that we need to work together to fight fraud, that is why we have fostered strategic partnerships with leading global organisations like BT, Genesys and Amazon. Together, we protect the UKs largest retail banks, investment banks and emergency services. You will be part of a cross-functional team, working across a variety of tasks from data science research and model development through to platform implementation and maintenance. You will use your knowledge of machine learning algorithms, frameworks, and methodologies to research and develop models for our cloud-based authentication and fraud systems, continuously iterating and evaluating model performance using appropriate metrics. You will: Explore and visualise data to discover innovative features and potential data sources.Engineer datasets, develop data pipelines, perform feature engineering, and write code to train, deploy, monitor, and run real-time inferences.Build and monitor ML models, addressing issues such as overfitting, underfitting, data leakage, and drift.You will use your expertise in engineering and DevOps/MLOps to manage our machine learning platforms using AWS SageMaker and other AWS services. You will:Design, build, and improve scalable public cloud-based machine learning platforms.Develop robust data pipelines and workflows, contributing to platform reliability, scalability, and observability through effective monitoring, alerting, and performance tuning.How you'll workAll our teams are given the freedom and autonomy to pick their own technology stack based on their system’s requirements and preferences. Our technology vision and strategy encourages you to try the latest innovations, and we naturally gravitate towards serverless architectures where appropriate. We value clean, maintainable and robust code for our business critical systems. Some of the technologies currently used by the Intelligence Group are listed below - while mastery of all these areas isn't required, familiarity with as many as possible will be advantageous. Cloud and Infrastructure Infrastructure as Code: Amazon CDKML Platform: Amazon SageMaker (Sagemaker Studio IDE, Sagemaker Training / Processing / Pipeline / Endpoints, Feature Store, Model Registry, Model Monitor)Data Processing: Amazon Athena, Apache Iceberg, AWS Glue, Spark Machine Learning ML Frameworks: Scikit-Learn, Hugging FaceML Algorithms: Tree-based (XGBoost), Deep LearningModel Explainability: SHAP explanations Programming and Development Tools Python Data Processing Libraries, e.g. NumPy, Pandas, Matplotlib, LibrosaSQL Source Control and CI/CD GitHubDockerCircleCI Telephony Protocols Session Initiation Protocol (SIP)Contact Centre as a Service (CCaaS), e.g. Amazon Connect, Genesys Cloud Smartnumbers values diversity of experience. Candidates should have a strong combination of several of the following skills, competencies and experience: We expect that you will have around 2 to 3 years’ commercial experience across a range of platform engineering and data science responsibilities. The list below gives you an idea of the attributesyou’llneed, though we’renot expecting you to have deepexpertise across all aspects:Collaborative approach to working, preferring to discuss and brainstorm tasks with the rest of the team rather than working in isolation.Able to own tasks end-to-end, take responsibility for the quality of deliverables, and drive ML and MLOps best practices and tooling to consistently enhance our models and ML platform.More interested in finding good solutions, increasing knowledge, and communicating results than simply working fast or producing lots of code.Understanding of machine learning fundamentals: data analysis, feature engineering, algorithms, performance metrics etc.Understanding of software engineering fundamentals: clean code, source control, SOLID principles, design patterns, refactoring etc.Understanding of DevOps/MLOps practices: Infrastructure as Code, data pipelines, CI/CD, containerisation, orchestration/pipelines, system & model monitoring Comfortable digging deep into either datasets or system logs to understand root causes or improve system performance.Proficient in Python, SQL, and data/ML frameworks like Pandas, Scikit-Learn etc.Experience with ML techniques and strategies, such as classical ML, deep learning, clustering, ensembling etc.Experience with MLOps techniques and building andmaintainingscalable data pipelines and ML platforms.Experience with cloud services (preferably AWS) and infrastructure as Code(e.g. CDK, CloudFormation).Familiarity with security and data governance principles and practice. As well as a competitive salary of circa £55k per annum, we also offer a comprehensive benefits package, covering a variety of areas, both professional and personal. These benefits include: Hybrid working style, with the expectation of two days in the office (with a great City of London office base!)Family friendly benefits including paid parental leave policiesAn extensive health insurance policy for you, with an option to add your family members A workplace pension with Hargreaves Lansdown Life insurance of 4 x your salary A discretionary annual bonus of up to 10% of your salaryWeekly self-development time to spend exploring your professional development interests25 days of annual leave (plus bank holidays), your birthday off, and an opportunity to buy up to 5 days annual leave per yearA holistic wellbeing support plan encompassing a variety of offerings to assist you. We provide you with a monthly £50 allowance to fund activities to best support your wellbeing as well as workshops and training to provide tools and guidance. Additionally, there is a wide-ranging employee assistance programme available to advise on personal, family or financial matters and also fun social events during the year. We have a simple 4 stage application process: The interview process will be: Screening interview with the hiring managerTake home coding assessmentTechnical and competency-based interviewCulture & values interviews with HR and bar-raiserSmartnumbers is committed to promoting equal opportunities in employment. You will receive equal treatment regardless of age, disability, neurodiversity, gender, gender identity, gender reassignment, marital or civil partner status, pregnancy or maternity, race, colour, nationality, ethnic or national origin, religion or belief, sex and sexual orientation. We welcome all applications for this role. We are committed to providing reasonable support/adjustments in our recruiting processes. If you need support, please reach out to the hiring contact.

What this role is, and what else it is called

Employers in London advertise this kind of work as Data Analyst, Data Engineer, Senior Data Engineer and AI Engineer too, so it is worth searching more than one wording. It is a mid-level Data, AI & Machine Learning role in London, advertised as hybrid, splitting time between home and the office.

How this role compares to the market

111 live UK roles list Python right now, across 77 source domains. The median annual salary midpoint is £55,000. 66% of 62 roles with a known arrangement are remote. Browse Python roles.

95 live UK roles list AWS right now, across 67 source domains. The median annual salary midpoint is £55,000. 44% of 55 roles with a known arrangement are remote. Browse AWS roles.

London has 1,276 live IT vacancies across 692 source domains, with a median annual salary midpoint of £55,000. Browse London roles.

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Technologies mentioned

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