JOB DUTIES
We are looking for a Data Scientist / Machine Learning Engineer to develop and optimize data-driven solutions for banking clients, with a focus on propensity modeling, customer engagement, and market opportunity sizing.
In this hybrid role, you will combine data science and engineering skills to build, evaluate, and deliver production-ready machine learning models and data pipelines. Your work will support data-driven sales and marketing strategies, including Customer Relationship Management (CRM) and Next Best Offer (NBO) use cases.
The ideal candidate has strong Python and SQL skills, hands-on experience in machine learning and propensity modeling, and the ability to translate business requirements into practical, measurable solutions.
Key Responsibilities
1. Propensity Modeling & Ranking
- Develop and optimize propensity models to predict click-through rates (CTR), customer engagement, and conversion probabilities.
- Apply machine learning algorithms such as XGBoost and LightGBM to solve customer targeting and recommendation problems.
- Develop and optimize ranking strategies using metrics such as NDCG, MRR, and Precision@K.
2. Feature Engineering & Data Modeling
- Build and refine features from customer behavior, interaction signals, and contextual data.
- Perform data preparation, feature engineering, and data modeling to support reliable and effective machine learning solutions.
- Handle sparse, noisy, and large-scale datasets to improve model performance.
3. Model Evaluation & Optimization
- Define, monitor, and evaluate model performance using metrics such as CTR, conversion rate, NDCG, and AUC.
- Identify and mitigate popularity and exposure bias in recommendation and ranking systems.
- Implement diversity, re-ranking, and fallback strategies to improve recommendation quality.
- Design approaches to address cold-start scenarios involving new customers or new offers.
- Continuously improve model performance and customer engagement through data-driven experimentation.
4. Data Engineering & Production Deployment
- Build and maintain data processing and feature pipelines to support machine learning workflows.
- Develop scalable solutions capable of handling data and predictions for millions of users.
- Support real-time or near-real-time model inference where required.
- Collaborate on model deployment, monitoring, and performance optimization to ensure reliable production operations.
5. Business Collaboration
- Work closely with business and technical stakeholders to understand banking and financial business needs and translate them into modeling solutions.
- Communicate analytical findings, model performance, and business implications clearly to technical and non-technical stakeholders.
- Contribute to data-driven sales, marketing, and customer engagement strategies.
JOB REQUIREMENTS
- At least 3 years of experience in Data Science, with at least 2 years of hands-on Machine Learning experience.
- Strong proficiency in Python and advanced SQL skills, including querying and processing large, complex datasets.
- Proven hands-on experience building and delivering propensity models or similar production-oriented machine learning solutions.
- Practical experience with machine learning algorithms, particularly XGBoost and LightGBM.
- Strong understanding of data modeling, feature engineering, model evaluation, and machine learning fundamentals.
- A hybrid skill set combining data science and data engineering, with the ability to develop data pipelines and support production-grade ML solutions.
- Good analytical and problem-solving skills, with the ability to translate business problems into measurable modeling objectives.
- Good communication skills and the ability to collaborate effectively with both technical and non-technical stakeholders.
Preferred Qualifications
- Experience in banking or financial services, particularly in CRM, customer analytics, propensity modeling, or Next Best Offer (NBO) projects.
- Candidates without direct banking experience are also encouraged to apply if they have strong engineering experience or a good understanding of banking and financial business processes, especially experience working with data and machine learning models.
- Experience deploying and maintaining machine learning models in production, including MLOps practices.
- Hands-on experience with AWS cloud services, particularly AWS SageMaker.
- AWS certifications are an advantage.
- Familiarity with A/B testing and experimentation to evaluate model effectiveness.
- Experience building scalable data and ML pipelines for large-scale customer data.
BENEFITS
OPPORTUNITIES AND CHALLENGES:
Opportunity to work in a professional, modern and energizing setting.
ATTRACTIVE REWARDING POLICY AND WORK-LIFE BALANCE:
CONTACT AND APPLICATION
If you find this position a good fit for you, please send your application or reach out directly for more details: