Job Description

Fave is one of Southeast Asia\'s leading fintech platforms; providing a smart payment app for the smart generation of consumers to pay & save. Fave also empowers offline businesses to grow and digitally connect with their customers in a whole new way. Fave is on a mission to maximise joy & value in every shopping experience and accelerate the offline world\'s transition to the digital economy. Fave currently operates in 35 cities across Malaysia, Singapore, and India. If being a part of a digital revolution in the fastest-growing region in the world excites you, get in touch today! As a Data Scientist, you will:- Involve in the development of predictive and prescriptive models using statistical and machine learning techniques. Assist to analyze large and complex datasets to identify trends, patterns that provide actionable insights to business leaders. Utilize data visualization tools to communicate findings effectively. Design, develop, and implement supervised as well as unsupervised machine learning algorithms and models. Optimize models for performance, scalability, and interpretability. Work closely with cross-functional teams, including data engineers, product managers, and stakeholders, to define project objectives and requirements. Collaborate with data engineers to ensure data pipelines are efficient and reliable. Follow and promote Agile practices to ensure effective collaboration and project delivery Deploy models into production environments and monitor their performance. Maintain and update models as needed to ensure ongoing accuracy while addressing schema drift and concept drift Maintain detailed documentation of data science projects, including methodologies, code, and results. Share knowledge and best practices with the team. Provide guidance and mentorship to junior data scientists. Contribute to the development of the data science team\'s technical skills and expertise. Requirements:- Strong programming skills in languages such as Python, Scala, SQL Experience in Supervised (Random Forest, XGBoost, LightGBM et al.) as well as Unsupervised learning algorithms (Clustering, Dimensionality Reduction and Recommender Systems et al.) Proficiency in machine learning libraries and frameworks (e.g., scikit-learn, TensorFlow, PyTorch, PySpark). Solid understanding of statistical analysis and experimental design. Experience with big data technologies (e.g., Hadoop, PySpark) is a plus. Excellent communication skills, with the ability to convey technical concepts to non-technical stakeholders.

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