Machine Learning Engineer (Fraud)

💙 Our Mission

We have recently updated our Company Mission to reflect our ambitions to build the future of payments in Africa. You can read all about it here.

Why does our mission matter?

There are over 1.3 billion Africans in the world and is the fastest growing region in the world. Africa is also the most expensive place to send money to and trade with. Talent is everywhere but opportunity isn’t. How do we bridge this divide with a sense of purpose?

• Think about people living far away from their loved ones who are financially responsible and support them. Imagine how you would feel losing 8-9% of your money through fees. How do we reduce the cost of sending money home through technology?
• What about the future for businesses? Think about those who are trying to do cross-border business from Africa outbound to the rest of the world? Why is it so hard to trade today? Why do people have to send money with other people physically or cash on planes? How much money and time is lost while doing this?
• What about the reliability of trade? When payments are delayed, that means business is disrupted. This costs everyone money. It loses us trust of customers.
• What about the future of work? Think about those young TikTok influencers in Uganda who make amazing content, but can’t get paid because they don’t have Visa or Mastercards yet have mobile money?

NALA’s true impact will be measured by the opportunities we create for Africa to trade with the world and the world to trade with Africa.

🙌 Your mission

Keep NALA and our customers safe through the development and deployment of advanced fraud detection models.

🎯 Your responsibilities in this role

• Develop and Tune Fraud Detection Models:
• Design, develop, and fine-tune machine learning models for fraud detection, utilising state-of-the-art techniques and algorithms.
• Collaborate with cross-functional teams to understand business requirements and translate them into effective machine-learning solutions.
• Productionise Machine Learning Models:
• Take a lead role in deploying machine learning models into production, ensuring scalability, reliability, and efficiency.
• Implement robust and scalable solutions to integrate machine learning models into NALA's product ecosystem.
• Programming and Modeling:
• Proficient in Python for both data manipulation and machine learning model development.
• Utilise machine learning frameworks and libraries to build, train, and validate models.
• Backend Development:
• Work closely with the engineering team to integrate machine learning models into backend services.
• Develop and maintain backend components that actively leverage machine learning for real-time decision-making.
• Feature Engineering:
• Identify, extract, and engineer features that enhance the predictive power of machine learning models.
• Continuously iterate on feature sets based on model performance and changing fraud patterns.
• AWS Infrastructure:
• Set up and maintain machine learning infrastructure on AWS, ensuring high availability and scalability.
• Collaborate with DevOps engineers to optimise the deployment pipeline and automate model updates.

🔥 Must have job requirements

• Proven experience in building and deploying machine learning models in a production environment.
• Strong proficiency in Python, with experience in machine learning libraries such as TensorFlow, PyTorch, or scikit-learn.
• Deep domain expertise in Fraud and Financial Crime
• Experience in feature engineering, model evaluation, and tuning techniques.
• Experience with backend development and integrating machine learning models into production services.
• Familiarity with cloud platforms, particularly AWS.

💪 Nice to have job requirements

• Bachelor’s or Master’s degree in Computer Science, Data Science, or a related field.
• Experience in a high-growth, start up

🎤 Interview Process

You will need to first submit your application through our ATS Workable.

If successful you will be selected for our interview process which has 4 stages:

1. [30mins] Interview with the Talent Team
1. In this stage we want to get to know you a bit more and follow up on your experience and motivations. The best preparation would be to really know why you applied for the role (i.e. your application questions)
2. [45mins] Interview with the Head of Data
1. The Manager will go through a short exercise involving a fictitious task that could happen in the role.
2. The senior team member will go through our company values and talk to you about your experience working in fast-paced environments
3. [30mins] Expert Interview
1. The final interview will involve speaking with an external subject matter expert, they’ll help us assess your technical skillset
4. [1hour] Interview with the CTO
1. In this stage, we want to get to know you and your experience deeply and we will focus closely on your experience as detailed on your CV. The best preparation is to just know what's on your CV really well 👍

Due to the seniority and importance of this position, we will be also doing reference calls with your previous managers. We will share more information in the final stages of the interview process.

⭐️ Benefits

• 35 Days Off: Enjoy an amazing 35 days of holidays to unwind and explore.
• Birthday Leave: Celebrate your special day with a bonus day off to take off in that month
• Learning Budget: Fuel your growth with $1000 annually for learning and development.
• Hybrid Working: We work 3 days a week in the office (Mon, Wed, Fri)
• Sarabi (Simba’s Mother in The Lion King): Themed snacks and Friday lunch with a focus on building great working relationships with the team.
• Global Workspace: Get access to WeWork locations worldwide.
• Monthly Socials: Join fun social events every month for great times
• Free Coffee: Enjoy barista-style coffee at your fingertips.
• Office Fun: Play pool and table tennis when you need a break