Data Science Manager

Job Summary:

We are looking for a seasoned Data Science Manager with a minimum of 10 years of experience in Data Science and Machine Learning to lead our AI team. The ideal candidate will have a strong background in NLP, Transformers, Generative AI, Agentic Frameworks, Responsible AI and MLOps, as well as experience in deploying AI solutions across various cloud platforms. This role requires a combination of technical acumen, leadership skills, and a strategic mindset to drive AI initiatives that align with our business objectives.
Key Responsibilities:
Leadership: Direct and support the AI team in developing and implementing advanced AI/ML solutions, ensuring projects align with the company's strategic goals.
Project Management: Oversee the entire lifecycle of AI projects, from ideation to deployment, prioritizing tasks, and managing resources to meet deadlines and deliverables.
Technical Mastery: Utilize expertise in Python, NLP, Transformers, and Generative AI to lead technical innovation and maintain high standards of excellence within the team. Having experience building Gen AI Agent and multi-agents leveraging any of the frameworks like Langgraph, Autogen, CrewAI, swarms , autonomous agent.
Cloud and MLOps Integration: Leverage cloud platforms such as AWS for deployment and incorporate MLOps practices leveraging Sagemaker, bedrock to enhance model development, deployment, and monitoring.
System Architecture: Architect scalable, reliable, and high-performing AI systems that integrate seamlessly with existing infrastructure.
Stakeholder Collaboration: Engage with stakeholders to convert business challenges into analytical questions, fostering a data-driven culture and driving impactful solutions.
Team Development: Mentor team members, promote professional growth, and cultivate a culture of innovation and continuous improvement.
Ethical AI Implementation: Uphold ethical standards in AI practices, ensuring models are fair, transparent, and accountable.
Effective Communication: Develop and deliver clear, concise presentations to communicate complex technical details to diverse audiences, facilitating understanding and buy-in.

Requirements:

Degree in Computer Science, Engineering, or a related technical field at the bachelor's or master's level; candidates with a Ph.D. will be given preference.
At least 10 years of professional experience in the realm of Data Science and Machine Learning, including a leadership role with a documented history of success.
Expertise in Python programming and experience with AI/ML libraries such as TensorFlow or PyTorch.
Comprehensive understanding of Natural Language Processing (NLP) and hands-on experience with Transformer architectures.
Proficient in leveraging AWS cloud platforms for the deployment of AI-driven solutions. Manage AWS cloud infrastructure and services including S3, EC2, Lambda, RDS, Glue, Sagemaker, EMR, and Step Functions to build and deploy scalable machine learning models.
Solid grasp of MLOps concepts and practical experience with tools such as Docker, Kubernetes, and Git for operational efficiency.
Capable of architecting and executing CI/CD workflows and managing infrastructure as code using tools like Terraform or AWS CloudFormation.
Exceptional problem-solving abilities, analytical mindset, and effective communication skills.
Established expertise with a minimum of 8 years in a technical capacity concentrating on AI/ML applications.
Deep knowledge of AI/ML frameworks, NLP techniques, and Transformer-based model implementations.
Proficiency in MLOps methodologies, including familiarity with LLMOps.
Experience in developing APIs using frameworks such as FastAPI.
Understanding of container orchestration using AWS EKS and familiarity with infrastructure frameworks like Bedrock.
Oversee ethical AI practices, ensuring fairness, accountability, and transparency in AI systems.
Exposure in AI governance, implementing and monitoring responsible AI frameworks.
Manage risk and compliance with regulatory and ethical standards for AI deployment.
Exposure ethical decision-making in AI development and operational processes.

Technical Skills:

Mandatory:

Experience in end to end GenAI, NLP and AI/ML projects (design to implement to deploy) in banking industry
Platform experience like Azure/AWS/GCP/Custom
AI Strategy - Enterprise Patterns and Approach
LLM Architecture and models, AI/ML models
Prompt Strategies, Advance RAG Strategies, Fine Tuning Strategies
Vector DB/Graph DB
EDA and Data pipelines
Agentic frameworks like crew, autogen, langgraph, swarms, autonomous agent any Agentic framework

Good to have:

Multi Tenancy concept
Exposure to Cloud Services, Cloud Storage, Compute Services, Application Services
Exposure to MLOps, LLMOps
EIA, Performance Tuning, Cost Optimization (FinOps), CBA
RAI practice
Relevant Certifications
Domain Skills (in any of the below sectors):

Banking:

Retail/Consumer/personal Banking, Commercial/Corporate Banking, Investment Banking
Regulation and Compliance
Relevant Certifications

Insurance:

Insurance domain (KYC, Onboarding, Policy, Plans, Premium, Payments, Renewal, Claim)
Regulation and Compliance
Relevant Certifications

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