Assistant Manager – AI & Machine Learning

Job Description

Assistant Manager – Analytics | Mashreq

The Tone:
This is a full-time role at Mashreq, focusing on advancing digital banking capabilities through AI and machine learning. The company aims to enhance digital banking performance, customer engagement, and marketing effectiveness across its digital channels. This position is crucial for utilizing customer digital footprint data to generate insights, develop predictive models, and optimize digital marketing campaigns and customer journeys. The role supports business growth and customer experience initiatives by translating digital behavioral data into actionable insights.

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, and next-best-product recommendations.
• Digital Marketing Analytics: Support marketing teams in evaluating digital campaign performance using advanced analytics and AI-driven insights, building models for campaign targeting and marketing attribution.
• AI Engineering & LLM Applications: Support the design and development of AI-powered solutions using Large Language Models (LLMs) for digital banking use cases.
• Data Preparation & Feature Engineering: Extract, clean, and transform large datasets from multiple banking systems and digital platforms, developing feature engineering strategies for model performance.
• Collaboration with Business & Product Teams: Work closely with digital banking, marketing, and product teams to identify data-driven opportunities and translate business requirements into actionable insights.

The Must-Haves
• Background: Bachelor’s or master’s degree in Computer Science, Data Science, Artificial Intelligence, Statistics, Engineering, or a related quantitative field.
• Experience: Overall 9.0 years of experience in analytics in a Fintech and/or Retail Banking environment, with 6+ years of hands-on experience in liability portfolio management analytics.
• Skills: Strong proficiency in Python for machine learning and data analysis; 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; understanding of Large Language Models (LLMs) and their applications, including basic knowledge of prompt engineering, embeddings, and retrieval-augmented generation (RAG.
• Bonus: Knowledge of R; experience working with LLM APIs and frameworks (e.g., OpenAI APIs, LangChain); familiarity with digital analytics platforms such as Google Analytics or Adobe Analytics; experience analyzing customer digital behavior and clickstream data; ability to build dashboards and visualizations using Power BI, Tableau, or similar BI tools; good verbal and pictorial presentation skills; proficiency in using query languages such as SQL; good applied statistics skills, such as distributions, statistical testing, and regression.