Raiffeisen Bank Machine Learning Academy
Raiffeisen Bank este o banca universala, oferind o gama completa de produse si servicii de cea mai buna calitate.
Daca esti la inceput de cariera te invitam sa faci primul pas, cu un stagiu de practica sau un program de internship, sau identifica locul de munca ideal pentru tine si alatura-te echipei Raiffeisen Bank!
Totodata daca esti un profesionist cu experienta, la Raiffeisen Bank iti oferim permanent oportunitatile pentru a iti dezvolta constant competentele si a atinge urmatorul nivel.
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:
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
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!