Role Overview
As the Lead Quantitative Analytics Consultant, you will spearhead data science initiatives within a centralized analytics function, focusing on advanced modeling and generative AI applications. This position plays a pivotal role in shaping analytical strategy, guiding technical execution, and supporting operational risk oversight across enterprise functions. You will lead a team of junior data scientists while directly contributing to the design and deployment of scalable, responsible AI systems.
Key Responsibilities
- Lead end-to-end development of statistical, machine learning, and generative AI models, applying supervised, unsupervised, and semi-supervised techniques
- Translate business questions into analytical frameworks and deliver actionable insights through robust modeling
- Design and optimize Retrieval-Augmented Generation (RAG) pipelines using embeddings, vector stores, and reranking logic
- Collaborate with data engineers, business intelligence, and front-end developers to integrate models into production environments
- Ensure model governance, monitoring, and validation, with attention to bias, hallucination, and human-in-the-loop controls
- Manage full model lifecycle using MLOps and LLMOps practices, including experiment tracking, deployment, and performance optimization
- Perform exploratory analysis using Python, R, SAS, or SQL and prepare structured and semi-structured data for modeling
- Visualize findings using Tableau, Power BI, Shiny, or Dash and communicate results to technical and non-technical stakeholders
- Maintain project transparency, meet delivery timelines, and elevate analytical value through proactive problem solving
Required Qualifications
- Minimum of 8 years in data science or quantitative analytics with hands-on experience in modeling tools and programming
- Deep understanding of machine learning and deep learning principles, including model evaluation, overfitting, and training dynamics
- Proven expertise in prompt engineering, LLM behavior control, and structured prompting techniques
- Experience building and tuning RAG systems using vector databases and chunking strategies
- Strong proficiency in Python and experience integrating generative AI into scalable, production-ready systems
- Background in MLOps/LLMOps, including deployment, monitoring, cost, and latency optimization
- Experience with Oracle, Teradata, or SQL Server and advanced data manipulation in Excel
- Skills in data visualization using Tableau, Power BI, Shiny, or Dash
- Ability to conduct trend analysis, forecasting, and pattern detection in complex datasets
- Strong communication, consultative approach, and ability to present findings to leadership
Preferred Qualifications
- Experience deploying analytical outputs via HTML5, Shiny, or Django
- Familiarity with deep learning frameworks such as TensorFlow, Keras, or PyTorch
- Ability to interpret technical results for business audiences and inform decision-making
- Critical thinking and rapid understanding of business domains
- Knowledge of banking products including credit cards, mortgages, deposits, loans, or wealth services
- Understanding of risk, marketing, operations, or supply chain functions in financial services
- Capacity to manage multiple priorities and adapt to evolving project demands
- Proven ability to work independently and collaboratively across teams
Technical Environment
Primary tools include Python, R, SAS, Spark, H2O, Aster, Hortonworks, MapR, Oracle, Teradata, SQL Server, Tableau, Power BI, Shiny, Dash, HTML5, TensorFlow, Keras, and PyTorch.
Work Environment
This role is based in one of the following cities: Hyderabad, Bengaluru, or Chennai. Work mode is local with no specified flexibility.
Organizational Values
The organization emphasizes strong customer relationships, risk management, compliance, accountability, and proactive governance. Decision-making is grounded in sound risk principles, with a commitment to diversity, inclusion, and equal opportunity.
