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- Tasks: Develop and optimise fraud detection models using cutting-edge ML techniques.
- 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 hands-on experience required.
- Other info: Bonus points for experience with AWS and ML deployment tools.
The predicted salary is between 42000 - 84000 £ per year.
As a Staff Machine Learning Engineer in our Applied ML & Research team, you\’ll drive the development of cutting-edge machine learning solutions that power critical features across our online gaming platforms. Your work will directly impact platform security, user experience, and large-scale data-driven decision-making for hundreds of thousands of users daily.
This role blends hands-on technical work with strategic thinking. You\’ll lead by example, contribute high-quality code, and help shape the ML roadmap in the organization through cross-functional collaboration.
What you\’ll you be doing:
- Identify high-impact ML opportunities and influence stakeholders to prioritize and support these initiatives.
- Design and develop scalable machine learning models – including classifiers, regressors, and rule-based systems – to solve real-world problems.
- Own the full ML lifecycle: from data exploration and feature engineering to model training, evaluation, and deployment.
- Translate complex technical concepts into clear insights for both technical and non-technical stakeholders.
- Set and guide technical direction across ML projects, ensuring technical best practices as well as alignment with business goals.
- Mentor junior engineers and foster a culture of knowledge sharing and continuous improvement.
We\’re looking for someone with:
- Master\’s degree (or equivalent) in Machine Learning, Data Science, Statistics, Mathematics, Computer Science, or a related field.
- 7+ years of industry experience building and deploying ML models at scale.
- Proven ability to lead cross-functional technical initiatives and influence engineering strategy.
- Proficiency in Python (with libraries like PyTorch, XGBoost, Scikit-learn) and SQL.
- Strong experience with machine learning pipelines and orchestration tools such as Airflow, SageMaker Pipelines, or similar.
- Deep understanding of machine learning fundamentals, including experience with Large Language Models (LLMs) and other emerging ML technologies.
- A track record of shipping production-level ML products and maintaining high code quality.
- Excellent problem-solving skills and ability to scope and disambiguate complex ML projects into clear, achievable milestones.
Bonus points for:
- Familiarity with ML tooling such as MLflow, ZenML, or Metaflow.
- Hands-on experience with AWS services (e.g., EC2, EKS, CloudFormation, Cognito).
- Exposure to streaming data platforms like Kafka.
- Contributions to open-source ML projects or publications in ML conferences.
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Staff Machine Learning Engineer - Applied ML & Research employer: Happening
Contact Detail:
Happening Recruiting Team
StudySmarter Expert Advice 🤫
We think this is how you could land Staff Machine Learning Engineer - Applied ML & Research
✨Tip Number 1
Familiarise yourself with the specific tools and technologies mentioned in the job description, such as SQL, Spark, and Python. Having hands-on experience with these will not only boost your confidence but also allow you to discuss your practical knowledge during interviews.
✨Tip Number 2
Engage with the machine learning community by attending meetups or webinars focused on fraud detection and anomaly detection. Networking with professionals in the field can provide valuable insights and potentially lead to referrals.
✨Tip Number 3
Prepare to showcase your problem-solving skills by working on relevant projects that involve building and optimising ML models. Be ready to discuss these projects in detail, highlighting your approach to performance tuning and model deployment.
✨Tip Number 4
Brush up on your communication skills, especially when it comes to explaining complex data science concepts. Being able to convey your findings clearly to stakeholders is crucial, so practice articulating your thoughts in a straightforward manner.
We think you need these skills to ace Staff Machine Learning Engineer - Applied ML & Research
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 Python, SQL, and any tools mentioned in the job description.
Craft a Compelling Cover Letter: In your cover letter, express your passion for machine learning and how your skills align with the role. Mention specific projects or achievements that showcase your ability to develop and optimise ML algorithms, particularly in fraud detection.
Showcase Your Technical Skills: Clearly outline your proficiency in the required technologies such as SQL, Spark, and Python. If you have experience with tools like Airflow or SageMaker, make sure to highlight this, as it directly relates to the job responsibilities.
Demonstrate Problem-Solving Abilities: Provide examples of how you've tackled complex data science projects in the past. Discuss your approach to breaking down projects into manageable tasks and how you ensured successful outcomes, which is crucial for this role.
How to prepare for a job interview at Happening
✨Showcase Your Technical Skills
Be prepared to discuss your experience with machine learning algorithms, especially in fraud detection. Highlight specific projects where you've used Python, SQL, or tools like Airflow and SageMaker, and be ready to explain your approach to feature engineering and model optimisation.
✨Demonstrate Problem-Solving Abilities
Expect questions that assess your analytical thinking and problem-solving skills. Prepare examples of how you've tackled complex data science challenges, particularly in a production environment, and how you broke them down into manageable tasks.
✨Communicate Clearly
Since you'll be working closely with cross-functional teams, practice explaining complex technical concepts in simple terms. Be ready to discuss how you've communicated findings to stakeholders in the past, ensuring they understood the implications of your work.
✨Prepare for Scenario-Based Questions
Anticipate scenario-based questions related to fraud detection and data engineering. Think about how you would approach building a fraud detection model from scratch, including data collection, cleaning, and model deployment, and be ready to share your thought process.
