Assistant Manager – AI & Machine Learning

Job Description

Assistant Manager – Analytics | Mashreq

The Tone:
This is a full-time role at Mashreq. Mashreq focuses on enhancing digital banking performance, customer engagement, and marketing effectiveness. This role is crucial for developing advanced analytics and AI-driven solutions, leveraging customer digital footprint data to generate insights, build predictive models, and optimize digital marketing campaigns and customer journeys.

The TL;DR
• Role: Full Time
• Type: Full-time

• Mission: Enhance digital banking performance, customer engagement, and marketing effectiveness by developing advanced analytics and AI-driven solutions.
• Tech Stack: Python, SQL, R, Scikit-learn, TensorFlow, PyTorch, XGBoost, LightGBM, OpenAI APIs, LangChain, Pandas, NumPy, PySpark, Spark, Google Analytics, Adobe Analytics, Power BI, Tableau, SAS

What You’ll Actually Do
• 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, continuously monitoring and refining model performance.
• Digital Marketing Analytics: Support marketing teams in evaluating digital campaign performance using advanced analytics and AI-driven insights, building models for campaign targeting, customer propensity, and marketing attribution, and providing insights on channel effectiveness and ROI.
• AI Engineering & LLM Applications: Support the design and development of AI-powered solutions using Large Language Models (LLMs) for digital banking use cases, integrating AI models and APIs into banking platforms, and experimenting with prompt engineering.
• Data Preparation & Feature Engineering: Extract, clean, and transform large datasets from multiple banking systems and digital platforms, developing feature engineering strategies to improve machine learning model performance and working with data engineering teams for efficient data pipelines.
• Business Collaboration & Insights: Work closely with digital banking, marketing, and product teams to identify data-driven opportunities, translate business requirements into analytical models, and present findings and recommendations to stakeholders to support strategic decisions.

The Must-Haves
• Background: Bachelor’s or Master’s degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering, or a related quantitative field. The role focuses on leveraging customer digital footprint data for advanced analytics and AI-driven solutions within digital banking.
• Experience: Overall 9.0 years of experience in analytics within a Fintech and/or Retail Banking environment, including 6+ years of hands-on experience in liability portfolio management analytics. Proven experience with common data science toolkits like SAS, Python, and R, and proficiency in SQL for data extraction. Strong experience with statistical model development techniques such as decision trees, logistic regression, neural networks, and clustering.
• Skills: Strong proficiency in Python for machine learning and data analysis; experience with machine learning algorithms (including regression, classification, clustering, recommendation systems) and libraries like Scikit-learn, TensorFlow, PyTorch, XGBoost, or LightGBM; understanding of Large Language Models (LLMs) and experience with LLM APIs/frameworks (e.g., OpenAI APIs, LangChain) including basic prompt engineering and retrieval-augmented generation (RAG); experience working with large datasets using Pandas, NumPy, PySpark, or Spark.
• Bonus: Knowledge of R; familiarity with digital analytics platforms such as Google Analytics or Adobe Analytics for analyzing customer digital behavior; ability to build dashboards and visualizations using Power BI, Tableau, or similar BI tools; strong verbal and pictorial presentation skills.