Lead Data Scientist

Nu10; Bengaluru; On-site; INR 2,500,000 - 3,500,000; Full time

Job summary

Nu10 in Bengaluru is seeking an experienced Lead Data Scientist specializing in wholesale banking. The ideal candidate will lead data teams, manage end-to-end data science projects, and develop AI-driven solutions that meet business goals.

The role requires at least 8 years of experience in the field, with strong skills in machine learning and banking domain knowledge. Alongside mentoring, the Lead Data Scientist will enhance customer insights through advanced analytics.

Qualifications

• 8+ years of experience in data science and machine learning, with at least 3 years in a leadership role.
• Strong domain knowledge in wholesale banking, including regulatory compliance, risk analysis, and customer profiling.
• Expertise in AI/ML techniques such as supervised and unsupervised learning and natural language processing (NLP).

Responsibilities

• Lead end-to-end data science projects focused on wholesale banking domains.
• Develop and deploy machine learning models and AI-driven solutions.
• Manage and mentor a team of data scientists and machine learning engineers.
• Translate business requirements into data-driven solutions.

Skills

Data science

Machine learning

Leadership

Banking domain knowledge

Cloud platforms (AWS, Azure, GCP)

Python

R

SQL

Education

Bachelor’s or Master’s degree in Computer Science/Data Science/Statistics/Mathematics

Tools

Pandas

Scikit-learn

TensorFlow

PyTorch

MLOps frameworks

Job description

Job Title: Lead Data Scientist – Wholesale Banking

Employment Type: Full-Time

Experience Required: 8+ Years

About the Role

We are seeking a highly skilled and experienced Lead Data Scientist with a strong background in banking, specifically in wholesale banking. The ideal candidate will have deep expertise in data science, machine learning, and AI-driven solutions, coupled with leadership experience to drive data-centric initiatives. You will work closely with stakeholders, product owners, and technical teams to shape data strategies, lead AI projects, and deliver impactful insights that align with business objectives.

Key Responsibilities

• Lead end-to-end data science projects focused on wholesale banking domains, including risk management, customer segmentation, credit scoring, and fraud detection.
• Develop and deploy machine learning models and AI-driven solutions to solve complex business challenges.
• Collaborate with product owners, AI project managers, and cross-functional teams to align data strategies with business goals.
• Apply advanced analytics techniques to enhance customer insights, improve operational efficiency, and optimize financial outcomes.
• Manage and mentor a team of data scientists and machine learning engineers, fostering a culture of innovation and continuous learning.
• Ensure adherence to data governance, privacy regulations, and industry best practices.
• Translate business requirements into data-driven solutions and present actionable insights to senior stakeholders.

Required Skills & Qualifications

• Bachelor’s or Master’s degree in Computer Science, Data Science, Statistics, Mathematics, or a related field.
• 8+ years of experience in data science and machine learning, with at least 3 years in a leadership role.
• Strong domain knowledge in wholesale banking, including regulatory compliance, risk analysis, and customer profiling.
• Proficiency in Python, R, SQL, and relevant data science libraries (e.g., Pandas, Scikit-learn, TensorFlow, PyTorch).
• Hands-on experience with cloud platforms such as AWS, Azure, or GCP.
• Expertise in AI/ML techniques such as supervised and unsupervised learning, natural language processing (NLP), and generative AI.
• Familiarity with MLOps frameworks and deployment pipelines.
• Strong communication skills with the ability to present complex data insights to both technical and non-technical stakeholders.

Preferred Qualifications

• Experience working in wholesale banking or financial services.
• Knowledge of risk modeling, credit analytics, and fraud detection in banking environments.
• Exposure to AI governance, ethical AI practices, and regulatory standards.
• Experience leading cross-functional teams and managing AI-driven product lifecycles.