Data Scientist

Join the data science team at slice, a dynamic fintech company in Bengaluru, India, dedicated to building cutting-edge data science solutions across credit risk, payments, product customer experience, fraud, and AI. We tackle complex 0-1 problems and seek motivated individuals ready for a challenge. Our team plays a pivotal role in refining credit underwriting through alternative data, optimizing risk management, minimizing default rates, and ensuring a healthy credit portfolio.

As a Data Scientist at slice, you will be instrumental in shaping credit, fraud, and business strategies by developing intelligent, scalable, and automation-first solutions. This role embraces the power of AI and Large Language Models (LLMs) to automate workflows, boost productivity, and accelerate decision-making processes throughout the organization.

Key Responsibilities:

• Manage complete data science workflows, from data preparation and feature engineering to model development, evaluation, deployment, and ongoing monitoring.
• Develop and enhance credit underwriting and risk models, utilizing both traditional and alternative data sources.
• Design and implement AI/LLM-driven solutions to automate decision systems, including areas like fraud investigation, reporting, and content generation.
• Contribute to the automation of fraud and risk workflows to expedite decision-making and reduce manual intervention.
• Oversee model deployment and lifecycle management, ensuring smooth transitions from experimentation to production environments.
• Create tools and plugins to improve team efficiency, such as automated reporting modules, variable builders, and idea generation assistants.
• Collaborate with cross-functional teams (credit, product, marketing, risk) to transform business challenges into scalable Data Science and AI solutions.
• Ensure model compliance with regulatory standards, business objectives, and established risk frameworks.
• Monitor model performance post-deployment and implement continuous improvements.
• Utilize infrastructure, including GPU environments, to optimize training efficiency and experimentation cycles.

Qualifications:

• Bachelor's or Master's degree in a quantitative field such as Statistics, Computer Science, Engineering, or Economics.
• 2 to 5 years of experience in Data Science, Machine Learning, or related domains.
• Proficiency in Python programming, with practical experience in libraries like numpy, pandas, and scikit-learn.
• Strong understanding of machine learning techniques, including supervised methods (Linear/Logistic Regression, Tree Models, Random Forest, Neural Networks) and unsupervised methods (Clustering, PCA).
• Prior experience in credit risk, fraud analytics, or marketing analytics is highly advantageous.
• Familiarity with LLMs and Generative AI use cases, such as prompting, automation, and workflow integration, is a significant plus.
• Knowledge of feature engineering techniques and their associated trade-offs.
• Experience with model deployment, monitoring, and lifecycle management.
• Excellent problem-solving abilities and a design-thinking approach, with a focus on building simple, scalable, and high-impact solutions.
• Demonstrated high ownership, strong communication skills, and the ability to thrive in a fast-paced, cross-functional environment.
• A curious mindset with a drive to experiment with new AI/ML methodologies and tools.