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Staff Machine Learning Engineer
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Staff Machine Learning Engineer

Staff Machine Learning Engineer

Full-Time 36000 - 60000 ÂŁ / year (est.) No home office possible
H

At a Glance

  • Tasks: Develop and optimise fraud detection models using cutting-edge data analysis tools.
  • Company: Join Happening, a leader in online gaming and sports betting technology.
  • Benefits: Enjoy a full-time role with opportunities for remote work and innovative projects.
  • Why this job: Make a real impact by enhancing platform security for hundreds of thousands of users daily.
  • Qualifications: Master’s degree in a relevant field and 4+ years of experience in machine learning required.
  • Other info: Bonus points for experience with AWS and ML deployment tools.

The predicted salary is between 36000 - 60000 ÂŁ per year.

Happening Greater London, England, United Kingdom

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Happening Greater London, England, United Kingdom

Join to apply for the Staff Machine Learning Engineer role at Happening

As a Staff Machine Learning Engineer in our Fraud Prevention team, you will leverage your expertise to build robust fraud detection models that safeguard our platform in the fast-paced world of online gaming and sports betting. Through innovative data analysis and cutting-edge tools, you’ll play a key role in ensuring platform security and enhancing our services for hundreds of thousands of daily users.
What you’ll you be doing:

  • Fraud Detection Modeling
  • developing and optimizing traditional ML algorithms such as gradient boosted trees in fraud detection or anomaly detection domains
  • creating graph analyses and graph connection modelling
  • creating usable datasets for explorative analysis and turn clues into leads and insights for ML modelling
  • Data Engineering
    • using SQL, Spark or Python for explorative analysis and feature engineering in a heavy tabular data environment
    • building new and supporting existing Erlang based microservices that enable product features
    • data engineering and working with relational DBs including data cleaning and preprocessing for ML pipelines and productionalization of the data pipelines using Snowflake, Airflow or DBT or similar tools
    • designing and implementing efficient ML pipelines
  • Collaborative Innovation
    • creating impactful ML solutions and owing their implementation and improvements
    • autonomously breaking down and estimating data science projects into timed deliverables
    • working closely with cross-functional product and engineering teams
    • communicating complex findings to stakeholders in a clear, understandable manner
  • Performance Tuning
    • optimizing models, continuously measuring and reacting to improve performance of ML solutions in production

    We\’re looking for someone with:

    • A Master’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, or a related field
    • Minimum 4 years of real world experience
    • Strong analytical background with proven expertise in exploratory data analysis, feature engineering, and working with tabular datasets
    • Proficiency in Python (including libraries like Scikit-learn) and SQL, with solid experience in relational databases
    • Good knowledge and experience of the broader data science domains, especially large language models or computer vision models used for anomaly detection
    • A track record in deploying machine learning pipelines using tools such as Airflow, SageMaker Pipelines or similar
    • A talent for clean coding, simple solutions, automated testing and continuous deployment
    • A passion for working in the cloud and automation
    • Excellent problem-solving skills and the ability to break down complex data science projects into achievable deliverables
    • Investigative mindset and attentive eye that looks for clues in tabular, sequential datasets

    Bonus points for:

    • Experience with ML development and deployment tools such as ZenML, MLFlow, Airflow, …
    • Experience with AWS Services such as EC2, EKS, Cloudformation, Cognito, …
    • Experience with Kafka

    Seniority level

    • Seniority level

      Mid-Senior level

    Employment type

    • Employment type

      Full-time

    Job function

    • Job function

      Engineering and Information Technology

    • Industries

      Technology, Information and Internet

    Referrals increase your chances of interviewing at Happening by 2x

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    Staff Machine Learning Engineer employer: Happening

    Happening is an exceptional employer located in Greater London, offering a dynamic work environment where innovation thrives. As a Staff Machine Learning Engineer, you will be part of a collaborative culture that values creativity and problem-solving, with ample opportunities for professional growth and development in the rapidly evolving field of machine learning. Enjoy competitive benefits and the chance to make a significant impact on fraud prevention in the online gaming and sports betting industry, all while working in one of the world's most vibrant cities.
    H

    Contact Detail:

    Happening Recruiting Team

    View Happening Profile

    StudySmarter Expert Advice 🤫

    We think this is how you could land Staff Machine Learning Engineer

    ✨Tip Number 1

    Familiarise yourself with the latest trends in fraud detection and machine learning. Being able to discuss recent advancements or case studies during your interview can demonstrate your passion and knowledge in the field.

    ✨Tip Number 2

    Network with professionals in the industry, especially those working in fraud prevention or machine learning. Attend relevant meetups or webinars to make connections that could lead to referrals or insider information about the role.

    ✨Tip Number 3

    Prepare to showcase your technical skills through practical examples. Be ready to discuss specific projects where you've implemented machine learning algorithms or data engineering solutions, as this will highlight your hands-on experience.

    ✨Tip Number 4

    Research Happening's platform and its approach to fraud prevention. Understanding their specific challenges and how your skills can address them will help you tailor your responses and show that you're genuinely interested in contributing to their team.

    We think you need these skills to ace Staff Machine Learning Engineer

    Machine Learning Algorithms
    Fraud Detection Techniques
    Data Analysis
    Feature Engineering
    SQL Proficiency
    Python Programming
    Scikit-learn
    Data Pipeline Development
    Microservices Architecture
    Erlang
    Snowflake
    Airflow
    DBT
    Performance Tuning
    Cloud Computing
    Automated Testing
    Continuous Deployment
    Investigative Mindset
    Communication Skills
    Collaboration with Cross-Functional Teams

    Some tips for your application 🫡

    Tailor Your CV: Make sure your CV highlights relevant experience in machine learning, data engineering, and fraud detection. Use specific examples that demonstrate your expertise in algorithms, data analysis, and tools mentioned in the job description.

    Craft a Compelling Cover Letter: In your cover letter, express your passion for machine learning and how it relates to fraud prevention. Mention any specific projects or achievements that align with the responsibilities of the role, showcasing your problem-solving skills and collaborative spirit.

    Highlight Technical Skills: Clearly list your technical skills, especially in Python, SQL, and any relevant ML tools like Airflow or SageMaker. Provide context on how you've used these skills in previous roles to solve complex problems or improve processes.

    Showcase Your Analytical Mindset: Demonstrate your analytical abilities by discussing your experience with exploratory data analysis and feature engineering. Include examples of how you've turned data insights into actionable solutions, particularly in high-stakes environments like online gaming or sports betting.

    How to prepare for a job interview at Happening

    ✨Showcase Your Technical Skills

    Be prepared to discuss your experience with machine learning algorithms, particularly in fraud detection. Highlight specific projects where you've developed or optimised models, and be ready to explain the techniques you used, such as gradient boosted trees or anomaly detection.

    ✨Demonstrate Data Engineering Proficiency

    Since the role involves data engineering, make sure to talk about your experience with SQL, Spark, or Python. Discuss how you've built or maintained data pipelines, and share examples of how you've cleaned and preprocessed data for machine learning applications.

    ✨Communicate Clearly

    You’ll need to convey complex findings to stakeholders, so practice explaining your work in simple terms. Prepare to discuss how you've collaborated with cross-functional teams and how you’ve broken down complex projects into manageable tasks.

    ✨Prepare for Performance Tuning Questions

    Expect questions on how to optimise machine learning models in production. Be ready to discuss your approach to measuring performance and the steps you've taken to improve model accuracy and efficiency over time.

    Staff Machine Learning Engineer
    Happening
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