WeRize - Lead Data Scientist - AI/ML Solution

About the role :

You will help solve problems at WeRize across credit/fraud risk, business, collections, customer service, etc through AI/ML techniques.

As a key member of the team, you work closely with leadership and business/functional units to manage model lifecycle of build, validate, implement, monitor, and update.

Responsibilities :

- Solve business problems across all functions using AI/ML.

- Ownership of model lifecycle management.

- Guide and train junior team members.

Key Responsibilities :

- Build AI/ML solutions to solve business problems across credit risk, fraud, collections, customer service, and other business functions.

- Own the complete machine learning model lifecyclefrom problem definition and model development to deployment, monitoring, and optimization.

- Develop predictive and prescriptive models using structured and unstructured data.

- Collaborate with business stakeholders to translate business challenges into data-driven solutions.

- Mentor and guide junior data scientists and analysts.

- Present analytical insights and recommendations to technical and non-technical stakeholders.

Required Skills :

- 4 to 8 years of hands-on experience in Data Science, Machine Learning, or Applied Analytics.

- Strong proficiency in Python (mandatory) and SQL.

- Experience building and managing ML models across supervised and unsupervised learning using structured and unstructured data.

- Hands-on experience with one or more of the following: Credit Risk Scorecards, Fraud Detection, Propensity Models, Optimization, NLP, Classification, Regression, Clustering, and Model Lifecycle Management.

- Strong analytical, quantitative, and problem-solving skills with the ability to translate business problems into scalable AI/ML solutions.

- Excellent written and verbal communication skills with experience working directly with business stakeholders.

- Self-driven, comfortable working in ambiguous environments, and capable of mentoring junior team members.

- Demonstrable experience in financial services, fintech, banking, or lending with exposure to financial domain-specific model development.