Machine Learning Engineer - Intelligence Group

Smartnumbers; Greater London; Hybrid; GBP 49,500 - 60,500; Full time

Benefits offered by this job

Hybrid working style

Health insurance

Pension plan

Annual bonus potential

Self-development time

25 days annual leave

Job summary

Smartnumbers is seeking a skilled Machine Learning Engineer to join their team in London. In this role, you will develop and optimize machine learning models for authentication systems, ensuring efficient and secure processes. You will work collaboratively across data science and engineering tasks, utilizing AWS services and contributing to innovative fraud prevention technologies.

Ideal candidates should have 2-3 years of experience in platform engineering, strong coding skills, especially in Python and SQL, and a solid understanding of machine learning algorithms and practices. A competitive salary and a comprehensive benefits package are offered.

Qualifications

• 2-3 years’ experience in platform engineering and data science responsibilities.
• Proficient in Python and SQL, with knowledge of ML frameworks.
• Experience with cloud services and infrastructure as code.

Responsibilities

• Develop ML models for cloud-based authentication and fraud systems.
• Engineer datasets and develop data pipelines.
• Manage machine learning platforms using AWS services.

Skills

Machine Learning fundamentals

Python

SQL

DevOps/MLOps practices

Data Analysis

Education

2-3 years of commercial experience in platform engineering

Tools

AWS

Scikit-Learn

Pandas

Job description

About Smartnumbers

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.

• 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.

• 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.

Technology Stack

• 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

What You’ll Need For The Role

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 attributes you’ll need, though we aren’t expecting you to have deep expertise 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 and maintaining scalable 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.

What We Can Offer You

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.

The application process

We have a simple 4 stage application process:

• Screening interview with the hiring manager
• Take home coding assessment
• Technical and competency-based interview
• Culture & values interviews with HR and bar‑raiser

Smartnumbers 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.

Please see our privacy notice: https://smartnumbers.com/privacy-notice/

About Us

We help companies in the fight against fraud. Our solutions help protect organisations from downstream fraud by ensuring the contact centre stays secure. Through our consortium of customers and partners, we enable organisations to work together to fight fraud by sharing intelligence and best practice.

As a software company with a telecommunications pedigree, we create market-leading security solutions for the contact centre. It’s why more than a thousand organisations trust us to help them fight fraud.

Our cloud-based AI-powered platform uses direct access to the carrier network, shared data on known fraudsters from our consortium and machine learning technology to protect your contact centre and your customers.

Find out more on how Smartnumbers helps organisations to prevent fraud and authenticate customers in one go here.