Assistant Manager - Analytics\\ RBG - Analytics

Mashreq; Bengaluru; On-site; INR 1,800,000 - 3,000,000; Full time

Job summary

Mashreq is seeking an Assistant Manager for AI & Machine Learning to build advanced analytics, predictive models, and AI-enabled banking solutions. You will leverage digital footprints to optimize customer journeys, campaigns, and engagement across channels.

The role emphasizes AI engineering, LLM applications, data prep, feature engineering, and collaboration with digital banking, marketing, and product teams to translate data into business impact.

Qualifications

• Bachelor’s or Master’s degree in CS/DS/AI/Statistics/Engineering.
• 9+ years of analytics experience in Fintech or Retail Banking.
• 6+ years hands-on liability portfolio analytics experience.
• Experience with SAS, Python & R.
• Proficiency in SQL.
• Strong statistics fundamentals and model development skills.
• Scripting and programming proficiency.

Responsibilities

• Analyze liability portfolio for revenue opportunities.
• Proactively generate new analyses and insights.
• Recommend analytics-driven strategies and own end-to-end analytics delivery.
• Execute portfolio management strategies and track results.
• Present results to senior management.
• Create statistical models to support decision making.
• Derive insights on customer segments and behavior patterns.
• Perform ad-hoc analyses and present findings clearly.
• Generate and track cross-sell leads as a business strategy.
• Participate in prioritization meetings and deliver as planned.

Skills

Python

SQL

R

ML algorithms

Scikit-learn

TensorFlow

PyTorch

XGBoost

LightGBM

LLMs

Prompt engineering

Embeddings

Pandas

NumPy

PySpark

Power BI

Tableau

Education

Bachelor's or Master’s in Computer Science / Data Science / AI / Statistics / Engineering

Tools

SAS

OpenAI APIs

LangChain

LLM APIs

Spark

Job description

Job Description:

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 field

Responsibilities

• Analyze liability portfolio for revenue opportunities
• Proactively come up with new ideas for analysis
• Recommend new strategies, get buy-in from business stakeholders and have full ownership end to end from analytics perspective.
• Execute agreed portfolio management strategies and track results for continuous improvement
• Present results to senior management
• Create statistical models to enable better decision making
• Derive insights about customer segments and behavior patterns
• Doing ad-hoc analysis when needed and presenting results in a clear manner
• Generate and track cross sell leads as a business strategy
• Participate in business prioritization meetings and deliver against plan
• 5

Qualifications

• Overall 9.0 years of experience in analytics in a Fintech and/or Retail Banking environment
• 6+ years of hands on experience in liability portfolio management analytics
• Experience with common data science toolkits like SAS, Python & R
• Proficiency in using query languages such as SQL
• Good applied statistics skills, such as distributions, statistical testing, regressing etc.
• Strong experience with statistical model development techniques like decision trees, logistic regression, neural networks, clustering etc.
• Good scripting and programming skills