Job Description
Job Description
We are looking for a Data Modeler to help us unlock our vast amounts of data with modelling and advanced analytics, allowing us to make smarter decisions and deliver even better products to our clients. Your primary focus will be in applying data mining techniques, doing statistical analysis, and building high-quality prediction systems and automated scores using machine learning, integrated with our products. You will also support our business teams, pre-sales and analytics to ensure product creation.
Suited to someone who has 3 to 4 years' experience core analytics function of a large bank / consulting firm and with experience handling large data volumes using data analysis tools and generating good data insights.
What you'll do
Deliver high quality analytical and value-added services for the clients while following timelines as promised to the client. You'll play an important role to Engage Business/Technical Consultants and delivery teams so that there is a shared understanding and agreement as to deliver proposed solution You will Use analysis and customer insights to develop value propositions for customers Develop and implement analytic models to detect and prevent fraud across products and fraud types Design, build and implement existing and new fraud prevention solutions for all fraud problems. Periodically monitor and optimize fraud solutions to protect clients. Ensure adherence of models as per regulatory model governance standards. Work with key internal and external stakeholders and third parties to ensure a clear view on fraud product performance and their impact on business. Collaborate with clients to drive initiatives that influence fraud solutions and provide strategic support to incorporate fraud solutions. Keep up to date on the latest fraud trends and technologies in data analytics and fraud detection Provide SME expertise for fraud prevention and strategic usage of Experian fraud products to clients. Qualifications
What you'll need to bring to the party
Understanding of machine learning techniques and algorithms, such as Gradient Boost, KNN, etc. Knowledge of different modelling frameworks like Linear Regression, Logistic Regression, Decision Tree Decent understanding and hands-on of statistical modelling/data analysis tools especially Python, Pyspark or SAS and market trends Applied statistics skills, such as distributions, statistical testing, regression etc. Understanding of decisioning and portfolio management in banking and financial services would be added advantage Understanding of credit bureau would be an added advantage Additional Information
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