Job Purpose:

To transform raw data into valuable insights for organizational growth and competition. Interpret and analyze data from multiple sources to devise innovative solutions. Build AI models and applications to solve complex banking business problems.

Job Responsibilities:

- Product Development, focusing on Machine Learning Engineer tasks.

- Build, train, and deploy ML models for inferencing, creating surrounding APIs for cloud and on-prem access.

- Develop ML-based applications for the bank using various ML techniques.

- Build and deploy ML applications in the cloud utilizing NLP, LLMs, and supervised learning.

- Understand the bank's data domain for EDA and MI reports, designing and developing ML applications.

- Leverage data to impact the economic ecosystem of the bank and Indian financial space.

- Architect and oversee the end-to-end development of machine learning pipelines to solve complex predictive and prescriptive modeling problems for enterprise clients.

- Lead the integration of Generative AI frameworks into existing product ecosystems to enhance automation and user engagement.

- Mentor a team of high-performing data scientists, fostering a culture of technical excellence and continuous learning through rigorous code reviews and architectural guidance.

- Communicate complex technical findings and model performance metrics to non-technical stakeholders to drive informed decision-making at the leadership level.

- Optimize existing NLP and deep learning models to improve latency, accuracy, and scalability across high-volume production environments.

- Collaborate with data engineering teams to ensure data quality, pipeline reliability, and seamless deployment of models into cloud-based production systems.

Educational Qualifications:

- Bachelor's degree in Computer Science, Engineering, Data Science, Machine Learning, or relevant field.

- Graduate degree in Data Science or another quantitative field is preferred.

Key Skills:

- Strong knowledge of Python and cloud-based deployments.

- Expertise in EDA, NLP, Topic Modeling, NER, Embedding models, and Semantic Search techniques.

- Working knowledge of LLMs via APIs (e.g., Open AI) and deploying open-source LLMs (e.g., LLAMA2, FLAN T5).

- Proficiency in Lang Chain framework and Fast API of Python.

- Working experience with Cloud (preferably Azure); understanding of MLOps frameworks is a plus.

- Knowledge of Classification, Q&A networks using BERT.

- Strong knowledge of TensorFlow and PyTorch frameworks.

- Experience with business intelligence tools (e.g., Tableau) and data frameworks (e.g., Hadoop) is a plus.

- Understanding of machine-learning and operations research.

- Analytical mind, business acumen, problem-solving ability.

- Excellent communication and presentation skills.

Experience Required:

- 11+ years of proven experience as a Data Scientist or Data Analyst.

Major Stakeholders:

- Branch Banking Team

- Alternate Channels (VRM, COP, Prime, Unified, BEU, TSAU, CLCL, ME)

- Credit / RIC

- IT - BSG / TSG

- Product teams

- Operations

- Sales Excellence Team

- RBCU

- Finance

- BIU TFG Product team

- Compliance

- Policy

Required Skillset:

- Demonstrated expertise in designing and deploying advanced machine learning models, with a deep understanding of Python-based data science stacks.

- Proven ability to implement and scale NLP and Generative AI solutions within enterprise-grade applications.

- Strong leadership capabilities, with the ability to manage technical teams and influence stakeholders across diverse organizational levels.

- Exceptional communication skills, capable of articulating complex algorithmic concepts to business leaders and technical peers alike.

- A minimum of 11 to 14 years of professional experience in data science, machine learning, or artificial intelligence roles.

- A Master's degree or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field from a premier institute.

- Ability to thrive in a hybrid work environment in Gurugram, maintaining high levels of productivity and collaboration across distributed teams.