Role Overview :

The Emerging Technology Standard Delivery team is responsible for driving the adoption of cutting-edge technologies and building enterprise-grade AI and data solutions that support Data & Analytics (D&A) Operations. As a Lead Data Scientist, you will collaborate with business, operations, and technology teams to design and implement data-driven solutions that create measurable business value and enhance customer outcomes.

This role requires strong expertise in Data Science, Machine Learning, Natural Language Processing (NLP), Deep Learning, and Generative AI, along with the ability to understand financial data and business processes. The ideal candidate should be passionate about emerging technologies, capable of leading high-performing teams, and experienced in delivering scalable AI solutions in production environments.

Key Responsibilities:

1. Technical & Team Leadership:

- Lead, mentor, and develop a team of senior data scientists by providing technical guidance, coaching, and performance management.

- Drive end-to-end execution of AI and Data Science initiatives while ensuring timely, high-quality delivery.

- Define technical roadmaps and oversee the development of enterprise-scale AI and analytics solutions.

- Build and enhance advanced data management frameworks covering data ingestion, transformation, quality, and workflow automation.

- Promote engineering best practices, coding standards, model reproducibility, and technical excellence across the team.

2. Business Collaboration & Stakeholder Management:

- Partner with business leaders and domain experts to identify high-impact use cases and define AI/ML strategies.

- Convert business requirements into scalable technical solutions and measurable success criteria.

- Present technical insights, project updates, and business outcomes to leadership and cross-functional stakeholders.

- Influence strategic decisions through data-driven recommendations and technical expertise.

3. AI/ML Solution Development:

- Design, develop, deploy, and optimize production-ready Machine Learning models using Deep Learning, NLP, Large Language Models (LLMs), Generative AI, and Retrieval-Augmented Generation (RAG).

- Evaluate emerging AI technologies and third-party platforms to support build-versus-buy decisions.

- Improve model performance, scalability, monitoring, and continuous optimization.

- Ensure Responsible AI practices including governance, explainability, documentation, and compliance.

4. Data Engineering & Analytics:

- Work extensively with structured, semi-structured, and unstructured datasets including financial documents, PDFs, and scanned content.

- Apply advanced techniques such as web scraping, crawling, entity extraction, and intelligent data preprocessing.

- Collaborate with Data Engineering teams to build scalable and reliable data pipelines supporting enterprise analytics.

5. Cloud & MLOps:

- Lead deployment of AI/ML solutions on cloud platforms such as AWS or Azure.

- Ensure adherence to MLOps best practices including CI/CD pipelines, automated model deployment, monitoring, and lifecycle management.

- Partner with platform engineering teams to improve model observability, reliability, and scalability.

6. Innovation & Continuous Improvement:

- Stay updated with the latest developments in AI, Machine Learning, NLP, Cloud Computing, and Financial Analytics.

- Drive innovation by encouraging experimentation with new technologies and methodologies.

- Continuously improve frameworks, tools, processes, and modeling practices across the Data Science organization.

Required Skills & Experience:

- 812 years of experience in Data Science, Machine Learning, Advanced Analytics, or Statistical Modeling.

- Proven experience delivering end-to-end AI/ML solutions from problem definition through production deployment and ongoing optimization.

- Strong expertise in Machine Learning, Deep Learning, Natural Language Processing (NLP), Large Language Models (LLMs), Generative AI, and Retrieval-Augmented Generation (RAG).

- Strong proficiency in Python and ML frameworks such as TensorFlow, PyTorch, and Scikit-learn.

- Experience with cloud platforms including AWS and Azure.

- Solid understanding of statistics, data engineering, feature engineering, and large-scale data processing.

- Ability to define AI strategies, model evaluation metrics, performance benchmarks, and technical trade-offs.

- Experience implementing Responsible AI, governance, explainability, and compliance standards.

- Hands-on experience with MLOps, CI/CD pipelines, and production deployment.

- Excellent stakeholder management, leadership, communication, and mentoring skills.

- Ability to translate business requirements into scalable AI solutions and influence cross-functional teams.

Preferred Qualifications:

- Experience working within Investment Banking or Financial Services organizations.

- Experience establishing enterprise AI governance, ethics, and regulatory compliance frameworks.

- Published research papers, patents, or conference presentations in AI, ML, NLP, or related domains.

- Experience managing globally distributed or matrixed teams.

- Recognized as a technical expert or thought leader within the AI community.

Education:

- Bachelor's/Master's degree in Computer Science, Statistics, Mathematics, Data Science, Artificial Intelligence, or a related Engineering discipline.

- Data Science or AI certifications are an added advantage.

- Strong programming knowledge in Python, SQL, and R.

Key Requirements:

- Notice Period: Immediate to a maximum of 60 days (Immediate joiners preferred).

- Work Mode: Hybrid 3 days Work from Office.

- Work Timings: 12:00 PM 9:00 PM.

- Work Location: RMZ, Bangalore.

Mandatory Competencies:

- Strong Communication Skills.

- Stakeholder Management.

- Team Leadership/People Management.

- Technical Expertise relevant to the role.

Interview Process:

- Interview Start Date: From 11th July.

- Interview Mode: Face-to-Face (Mandatory).

- Location: Bengaluru.

Selection Process:

1. Codility Coding Assessment.

2. Technical Interview.

3. Managerial Interview.