About Banking Processes and Operations

As part of our ongoing digitalization transformation, we are optimizing and redefining the way that banking processes are handled. We are implementing advanced workflow management systems to streamline operations, reduce manual tasks, and boost efficiency.

Advanced Optimization Algorithms and AI play a crucial role in this transformation.

Your profile

Experienced Data Scientist, able to conduct research; generate requirements; solution design; technical concepts and architectures; and delivery of solutions.

We`ll trust you to:

• Smoothly integrate within an Agile Development Team, consisting of DEVs, QAs, BAs, Data Scientists.
• Maintain and enhance existing Optimization Algorithms (Constraint Integer Programming) based on SCIP.
• Conceptualize and design ML models to optimize and transform bank processes in the Operations Area
• Contribute on both technical and business process transformation by introducing data science & ML/AI with focus on language models, pattern recognition, forecasting, optimization methods and reinforcement learning agents.
• Come with a good understanding of the current software development state of the art and contribute to architectural design of the automation of processes and workflows in a microservice environment.
• Translate business requirements into technical concepts; research and implement appropriate ML algorithms; apply suitable frameworks for solving constraint integer programs.
• Monitor performance of the models in production; ensure no degradation occurs below established metrics and retrain when needed.
• Work with Data Engineers to deploy and maintain models in production or to analyze/build data pipelines for new data sources.

We`d love you to bring:

• In-depth knowledge of mathematics, statistics, and algorithms.
• Ability to write robust code in Python and use version control systems such as Git
• Proficiency in Data Science:
• Reflex Models - Supervised Learning/ Unsupervised Learning Methods for Regression and Classification; Gradient Boosting Models (LightGBM, XGBoost), Deep Learning Architectures with Neural Networks (ConvNets, Attention Models - Transformers, Recurrent Layers - GRU, LSTM
• State Based Models - Markov Decision Process - Reinforcement Learning and Deep Reinforcement Learning
• Variable Based Models - Constraint Satisfaction Problem - Large Scale Optimization Methods with Non-Linear Programming and Mixed Integer Linear Programming
• Advanced Topics: Meta Learning and Multitask Learning, Transfer Learning, NLP, Large Language Models, Generative AI, Fine Tuning and Few Shot Learning
• Python Libraries and Frameworks: scikit-learn, pytorch, keras, tensorflow, transformers, scipy, numpy, pandas, h5py, hyperopt, pyomo, ipopt, rlutil, lightgbm, langchain, huggingface
• Proficiency in Software Development with Python:
• Microservices Arhitecture and Environment: Flask, Django, FastAPI with Design Patterns and Security Frameworks
• CI/CD with Jenkins/Teamcity
• Kubernetes/Docker Platform
• Experience with training, evaluation and deployment of ML models
• Recommended experience with GCP, AWS or Azure Platform for Big Data and ML/AI Capabilities
• Awareness/information about DevOps practices like CI/CD

Nice to haves:

• Experience with Ops Transformation in banking sector
• Graduated AI, Data Science, ML, NLP, Statistics courses/certifications from a major university.
• Hands on knowledge of Shell Scripting
• Experience working in an Agile setup, practicing Scrum or Kanban
• Familiarity with Atlassian stack: Jira, Confluence, Bitbucket
• Familiarity with Elastic Search
• Familiarity with Camunda 7 and 8
• Familiarity with Kafka Event Streaming Methodology

We strive for a culture in which we are empowered to excel together every day. This includes acting responsibly, thinking commercially, taking initiative and working collaboratively.