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) 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.

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.

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.

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

Analyze liability portfolio for revenue opportunities

Proactively come up with recent 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

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

Robust verbal and pictorial presentation skills preferred

📌 Assistant Manager - Analytics..RBG - Analytics (Bengaluru)
🏢 Mashreq Corporate u0026 Investment Banking Group
📍 Bengaluru