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Lead Data Scientist at IDFC FIRST Bank — Mumbai

Experience6–10 yrs
SalaryNot disclosed
Work modeonsite
Levelsenior
Posted11 Sept 2026

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About IDFC FIRST Bank

IDFC FIRST Bank is hiring in Mumbai in financial services. This role looks for around 6+ years of experience.

Skills

machine learning
deep learning
statistical modeling
data preprocessing
feature engineering
exploratory data analysis
MLOps
model deployment
model tracking
automation
API design
model monitoring
data quality
reconciliation

The role

A data scientist at a financial services company builds machine learning models for credit risk, fraud detection, churn prediction, and customer personalization, applying deep learning and statistical modeling to banking decisions. The role also advances MLOps and Generative AI for production deployment and enterprise data initiatives.

Full job description

Job Requirements

About the Role

The Lead Data Scientist is responsible for identifying business needs and delivering impactful data-driven solutions through advanced analytics and machine learning. This role involves building analytical models, developing test strategies, and supporting senior analysts in achieving business outcomes. The Lead Data Scientist also plays a key role in leading technical teams, driving innovation, and ensuring seamless deployment of machine learning systems across environments.

Key Responsibilities

Primary Responsibilities

Lead the design and development of advanced ML/AI models for credit risk, fraud detection, churn prediction, and customer personalization.Translate complex data science outputs into business insights and strategic recommendations for senior management.Oversee end-to-end model lifecycle — from problem definition, data preparation, model building, validation, to deployment.Ensure models comply with regulatory requirements and internal governance standards.Collaborate with product, risk, and business teams to identify high-impact use cases for AI/ML.Mentor and guide junior data scientists, reviewing their work and building best practices in modeling.Partner with data engineering teams to design scalable pipelines and enable real-time model deployment.Drive adoption of new techniques such as deep learning, NLP, and Generative AI for banking use cases.Establish model monitoring frameworks to track performance and mitigate risks.Contribute to strategic data initiatives like enterprise AI platforms, data lake integration, and advanced analytics adoption.Collaborate on the MLOps lifecycle, including model tracking, experimentation, and automation.Implement frameworks to ensure data quality and reconciliation checks are transparent and consistent.

Secondary Responsibilities

Provide technical expertise and lead process improvement initiatives.Supervise support resources for contract metrics and project assignments.Act as a team leader for large or complex projects, ensuring timely and quality delivery.Engage regularly with strategic business partners to address challenges and implement data-driven strategies.

What We Are Looking For

Education

Graduation: Bachelor of Science (B.Sc) or Bachelor of Technology (B.Tech) or Bachelor of Computer Applications (BCA)Post-Graduation: Master of Science (M.Sc), Master of Technology (M.Tech), or Master of Computer Applications (MCA)

Experience

6 to 10 years of relevant experience in data science, machine learning, and analytics.Proven track record of leading machine learning projects and deploying models in production environments.

Skills and Attributes

Strong expertise in machine learning, deep learning, and statistical modeling.Proficiency in data preprocessing, feature engineering, and exploratory data analysis.Hands-on experience with MLOps workflows, including model deployment, tracking, and automation.Ability to design and maintain APIs for model integration.Excellent problem-solving and analytical thinking skills.Strong leadership and team management capabilities.Effective communication and stakeholder engagement skills.Commitment to innovation and continuous improvement in data science practices.