Machine Learning Engineer - Intelligence Group

We are on a mission to stop fraud and improve customer authentication. 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.

What you'll be working on

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 work

All 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 CDK
• ML 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 Face
• ML Algorithms: Tree-based (XGBoost), Deep Learning
• Model Explainability: SHAP explanations

Programming and Development Tools

• Python
• Data Processing Libraries, e.g. NumPy, Pandas, Matplotlib, Librosa
• SQL

Source Control and CI/CD

• GitHub
• Docker
• CircleCI

Telephony Protocols

• Session Initiation Protocol (SIP)
• Contact Centre as a Service (CCaaS), e.g. Amazon Connect, Genesys Cloud

Do you have experience in Spark?, 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.

Pulled from the full job description

• Annual leave

• Life insurance

• Employee assistance programme

• Company pension, 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 policies

• An 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 salary

• Weekly self-development time to spend exploring your professional development interests

• 25 days of annual leave (plus bank holidays), your birthday off, and an opportunity to buy up to 5 days annual leave per year

• A 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.