Raiffeisen Bank Machine Learning Academy

Raiffeisen Bank, bancă universală de top, activează pe piața bancară din România de peste 25 de ani, deservind peste 2,3 milioane de clienți, persoane fizice și juridice. Banca are 4.930 de angajați în 291 de unități, 1130 ATM&MFM și 27.000 POS-uri.

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Raiffeisen Bank Machine Learning Academy is our newest trainee program where you will be able to pick-up the needed practical skills that will push your career ahead and give you a head start in the following three roles: Data Scientist, Machine Learning Engineer and ML Dev Ops.

For 12 months we will offer you a personalized learning path that will support you in building a solid foundation, where you will benefit from both internal and external trainings & learning resources and you will be mentored by highly experienced Machine Learning Engineers and you will get to work together with them on our use cases to get some practical experience under your belt.

As a Data Scientist/Machine Learning Engineer we'd like you to have:

Science/Math/Statistics skills:

Understanding and implication of common statistical linear models/approaches, regressions, correlations, regularization, Gauss-Markov theorem, regularization, decision tree

Understanding and implication of non-linear models: forests, clustering methods, non-linear regressions. Includes time-series analysis if applicable in the domain

Understanding and implication of advanced ML methods (neural networks, NLP, math optimization etc.)

Understanding and implication of AI methods (computer vision, reinforcement learning etc.)

Knowledge of models basic quality metrics (ROC-AUC, log loss, MAE, MSE, ..) and where which to apply

Skills in handling unbalanced data sets (eg undersampling, oversampling)

Bagging, boosting methods

Skills in data preparation: cleansing, feature selection & engineering

Skills with general cross-validation techniques

Ability to make mathematical/statistical analysis of experiment results


Tech/Computer Science skills:

Knowledge of latest libraries/technologies and methods (Python ecosystem)

Skills in programming DS models in scripting language (Python, Tensorflow, PyTorch)

Skills in distributed computing (processing of 'big data' dataset using PySpark, Impala, Hive etc.)

Ability to establish all relevant infrastructure yourself given permissions necessary

Data Visualisation skills (eg. Plotly, VueJS)

Skills in deployment models as service or in containers (eg. Docker, Django, Flask)

Skills in working with Machine Learning infrastructure on one major Cloud provider (eg. AWS, Google, Azure)

Skills in monitoring and updating models in production

Other skills in programming languages and tools that are either used of adjacent data science or ML dev ops are appreciated and considered an asset (eg. Git, Kubernetes, MLflow, Javascript, C++, CSS etc.)

Business skills:

Understanding how different models influence on business KPI and calculate real benefits of modeling usage after tests

Finding opportunity to improvement business KPI based on best practices across the companies and scientific discovery

Ability to design an experiment in business context

Ability to anticipate what value a ML model can bring to a business process (potential benefit), translate task from business to math language, see and negotiate risk in a project

Ability to present their result to business in an accessible way

Knowledge of the data, processes and limitations available in the Bank

Negotiate and educate business owners on DS development

Interviewing and mentoring skills for juniors

Management and coordination skills (ability to decompose the process of building a model (E2E) into subtasks for DS & DE, set task, monitor and control execution).

Make the first step in your career by applying for Raiffeisen Bank Machine Learning Academy!