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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 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
- 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
- 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
- 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
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Industries
Technology, Information and Internet
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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.
