About Gen
Gen is a global company dedicated to powering Digital Freedom through its trusted consumer brands including Norton, Avast, LifeLock, MoneyLion and more. Our combined heritage is rooted in financial empowerment and cyber safety for the first digital generations, and today we deliver award-winning cybersecurity, online privacy, identity protection and financial wellness solutions to nearly 500 million users in more than 150 countries.
Together, we share a collective passion and vision to protect consumers and help them grow, manage and secure their digital and financial lives. We're always looking for smart, fearless and high-impact talent who see AI as a teammate – leveraging it to move faster and deliver meaningful results.
When you're part of Gen, you'll have the flexibility, tools and support to do your best work and grow your career – from flexible working options and time off to competitive pay, benefits and well-being programs.
At Gen, we are scrappy and relentlessly customer driven. We create room for healthy debate, experimentation and continuous learning, and we seek out people with different experiences, identities and ideas to join our team. You'll work with people who back each other, respect each other and understand that our differences are a competitive advantage.
If this sounds like you, we'd love you to be part of Gen.
About The Role
The Kuala Lumpur office is the technology powerhouse of MoneyLion. We pride ourselves on innovative initiatives and thrive in a fast paced and challenging environment; join our multicultural team of visionaries and industry rebels in disrupting the traditional finance industry.
As a Data Scientist (Machine Learning), you will sit at the intersection of data, product, and engineering to design, build and validate models that drive measurable impact for our customers and business. You will own the end-to-end lifecycle of data science solutions – from problem framing and data exploration, to modeling, experimentation, and partnering with engineering teams to bring successful solutions into production. You will work closely with other Data Scientists, Machine Learning Engineers and MLOps Engineers to shorten the model development cycle and ensure our models are robust, explainable and well-governed.
Key Responsibilities
- Translate ambiguous business problems into well-defined analytical or machine learning problems with clear success metrics and hypotheses
- Explore, analyze and understand large, complex datasets; identify signal, data quality issues and opportunities for new features
- Design, build and validate models for use cases such as prediction, risk scoring, segmentation, recommendation and personalization, depending on product needs
- Own the full modeling workflow: feature engineering, model training, hyperparameter tuning, validation, performance analysis and documentation
- Design and analyze experiments (e.g. A/B tests) and offline evaluations to quantify the impact of models and policies, applying strong statistical rigor
- Collaborate with Machine Learning Engineers and MLOps Engineers to move successful models from notebooks to scalable, reliable production systems, including definition of SLAs, monitoring and alerting
- Monitor model performance and data quality in production, diagnose issues such as drift or degradation, and iterate on models as business and user behavior evolve
- Partner with Product, Engineering and other stakeholders to prioritize work, communicate trade-offs, and influence product and decision-making using data and experimentation
- Contribute to shared tools, libraries and best practices that improve the speed, quality and governance of model development across the team.
About You
- Strong quantitative background from a degree in Computer Science, Statistics, Mathematics, Engineering or a related field, or equivalent practical experience
- Hands-on experience building and deploying machine learning or statistical models to solve real-world problems, ideally in consumer, fintech or security domains
- Proficient in Python and SQL, with experience using common data and ML libraries (e.g. pandas, NumPy, scikit-learn, XGBoost, PyTorch or TensorFlow)
- Comfortable working with large datasets and modern data platforms; able to write efficient, well-structured queries and data pipelines
- Solid understanding of machine learning fundamentals (supervised/unsupervised learning, regularization, feature engineering, model evaluation and selection)
- Strong foundation in statistics and experimentation, including hypothesis testing, confidence intervals, experiment design and dealing with biases and confounders
- Familiarity with MLOps concepts and tooling (e.g. experiment tracking, model registries, feature stores, CI/CD for ML) and experience collaborating with platform teams is a plus
- Able to break down complex problems, explore solution options and make pragmatic trade-offs between complexity, performance and maintainability
- Clear and structured communicator who can explain technical concepts to both technical and non-technical audiences and influence decisions using data
- Thrive in a fast-paced, high-growth environment; you are curious, experiment-minded, collaborative, and comfortable with healthy debate and continuous learning
What's Next
- TA Screening Call
- Take-Home Assessment
- Interview & discussion of Take-Home Assessment with the Hiring Manager (F2F)
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Gen is an
equal opportunity employer, and we're committed to fair, inclusive practices at every stage of the candidate and employee journey. Employment decisions are based on merit, experience and business needs.
