Data Scientist / Machine Learning Engineer

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.

1. 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.

1. 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.

1. 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.

1. 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.

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.

Opportunity to work in a professional, modern and energizing setting.

• Challenging projects, possibilities, cutting-edge technology, and problems.
• An open and dynamic working atmosphere that promotes the interchange of ideas, while also empowering you to work and create in your own style. Each employee’s talent and accomplishments are valued, and outstanding employees are recognized and rewarded on a yearly basis.
• Opportunities for capacity development, as well as assistance with professional certification expenses, are available to those who work in the field (1 million - 5 million).
• Some advanced certifications will cover the full cost of studying and taking the exam.

ATTRACTIVE REWARDING POLICY AND WORK-LIFE BALANCE:

• Review salary 1-2 times/year, with the possibility of an unexpected salary rise based on capacity an infinite number of times per year
• Policy for 13th-Month Salary Bonus and Holiday Bonuses According to Company Regulations.
• Holiday travel mode (package 5-7 million/person), team building,…
• Full participation in social insurance, as well as a yearly health checkup at a reputable hospital.
• Comprehensive visit policy for personnel and relatives: birthday, marriage, childbirth, illness, etc.
• The football club, cycling car, badminton, picklepall,… are just a few of the sports and cultural activities available.
• Allowance for classes and training awards: Learning Star of the Month
• A chance to be the face of the year’s study, the royalties…