助理经理 - 分析\..RBG - 分析

Role Purpose
The Assistant Manager – AI & Machine Learning will support the development of advanced analytics and AI-driven solutions to enhance digital banking performance, customer engagement, and marketing effectiveness. The role focuses on leveraging customer digital footprint data across digital channels to generate insights, develop predictive models, and optimize digital marketing campaigns and customer journeys.
In addition to traditional machine learning expertise, the role requires foundational AI Engineering capabilities, including familiarity with Large Language Models (LLMs) and their application in banking use cases such as customer support automation, intelligent search, and digital engagement solutions.
The role requires strong analytical capability, technical expertise in machine learning, and the ability to translate digital behavioral data into actionable insights that support business growth and customer experience initiatives.
Ability to deliver Use cases in RM Efficiency/Productivity improvement
Have worked in SME Banking / SME Digital banking / Commercial Banking / Corporate Banking Portfolio and Identity Opportunities at scale.
Portfolio Analytics on CASA Portfolio to support CASA Squad and Product team
Bringing robust tracking and campaign fulfillment process for Liabilities /Trade Finance/ Working Capital/FX
Key Result Areas (KRA)
1. Machine Learning & Predictive Analytics

• Develop and deploy machine learning models to support digital banking use cases such as customer segmentation, churn prediction, next-best-product recommendations, and campaign targeting.

• Implement predictive analytics models to improve customer engagement and product adoption across digital channels.

• Continuously monitor model performance and refine algorithms to improve accuracy and business impact.

Digital Marketing Analytics

• Support marketing teams in evaluating digital campaign performance using advanced analytics and AI-driven insights.

• Build models for campaign targeting, customer propensity, and marketing attribution.

• Provide insights on channel effectiveness, campaign ROI, and customer acquisition strategies.

4. AI Engineering & LLM Applications

• Support the design and development of AI-powered solutions using Large Language Models (LLMs) for digital banking use cases.

• Integrate AI models and APIs into banking platforms and analytics workflows.

• Experiment with prompt engineering and model fine-tuning to enhance AI solution performance.

5. Data Preparation & Feature Engineering

• Extract, clean, and transform large datasets from multiple banking systems and digital platforms.

• Develop feature engineering strategies to improve machine learning model performance.

• Work with data engineering teams to ensure efficient data pipelines for analytics use cases.

6. Collaboration with Business & Product Teams

• Work closely with digital banking, marketing, and product teams to identify data-driven opportunities.

• Translate business requirements into analytical models and actionable insights.

• Present findings and recommendations to stakeholders to support strategic decisions.

Problem Solving & Decision Making
Analytical Problem Solving

• Analyze complex datasets to identify patterns, anomalies, and opportunities that improve customer engagement and digital banking performance.

• Apply statistical techniques and machine learning methods to solve real business challenges.

AI Solution Design

• Support decision-making related to the selection and application of AI/ML models

• Evaluate trade-offs between model performance, scalability, and usability in production environments.

Data Interpretation & Business Insights
· Translate complex analytical outputs into clear business insights that can inform marketing strategies and product development.
Technical Skills
Programming & Data Analysis

• Strong proficiency in Python for machine learning and data analysis.

• Experience with SQL for data extraction and manipulation.

• Knowledge of R is an added advantage.

Machine Learning & AI

• Experience with machine learning algorithms including regression, classification, clustering, and recommendation systems.

• Hands-on experience with libraries such as Scikit-learn, TensorFlow, PyTorch, XGBoost, or LightGBM.

AI Engineering & LLM Technologies

• Understanding of Large Language Models (LLMs) and their applications.

• Experience working with LLM APIs and frameworks (e.g., OpenAI APIs, LangChain, or similar frameworks).

• Basic knowledge of prompt engineering, embeddings, and retrieval-augmented generation (RAG).

• Familiarity with developing AI-enabled applications such as chatbots or knowledge assistants.

Data Processing
· Experience working with large datasets using Pandas, NumPy, and big data frameworks such as PySpark or Spark.
Digital Analytics Tools

• Familiarity with digital analytics platforms such as Google Analytics, Adobe Analytics, or similar tools.

• Experience analyzing customer digital behavior and clickstream data.

Data Visualization
· Ability to build dashboards and visualizations using Power BI, Tableau, or similar BI tools.
Skills & Competencies

• Strong analytical and problem-solving abilities

• Ability to work with large and complex datasets

• Understanding of digital customer journeys and online behavior analytics

• Foundational understanding of ML / Stats and AI engineering concepts and LLM applications

• Effective communication and presentation skills

• Ability to translate analytical insights into business recommendations

• Collaborative approach to working with cross-functional teams

Educational Qualifications
Bachelor’s or master’s degree in one of the following disciplines:

• Computer Science

• Data Science

• Artificial Intelligence

• Statistics

• Engineering or related quantitative fieldBottom of Form

Originally posted on Himalayas