Overview
GFT is a business change and IT company trusted by the world’s leading financial institutions to meet the demands of the digital revolution. Innovation and creativity are part of our DNA and drive our own success story.
Your main responsibilities:
Implement complete data science pipelines to solve business problems, including data extraction, data preprocessing, exploratory data analysis, feature engineering, machine/deep learning modeling, and application deployment.
Extraction and selection of features, building and optimizing supervised models using machine/deep learning.
Processing, cleansing and verifying data integrity used for the analysis processes.
Automate data science processes and ml-systems and constant tracking of its performance.
Research and analyze state-of-the-art machine learning techniques.
Write technical documentation and results.
Company:
GFT Group
Qualifications:
Minimum requirements:
Passion and enthusiasm for data science and machine learning.
3+ years of experience researching and/or developing applications in Python.
2+ years of experience solving data science problems in Python (Scikit-learn, TensorFlow, Keras, Pytorch, Pandas, Numpy, MatplotLib).
Demonstrable experience implementing supervised models using machine/deep learning techniques, including word embeddings, convolutional and recurrent neural networks, and gradient boosting.
Knowledge and experience processing structured and unstructured data, especially Natural Language Processing (text extraction, text-based feature engineering, …).
Solid understanding of machine/deep learning concepts like backpropagation, gradient descent, vanishing gradient problem, data leakage, stratified sampling, and data augmentation.
Good applied statistics skills, such as distributions, statistical testing, regression, etc. Bayes rule, Hypothesis tests.
Minimum requirements:
Passion and enthusiasm for data science and machine learning.
3+ years of experience researching and/or developing applications in Python.
2+ years of experience solving data science problems in Python (Scikit-learn, TensorFlow, Keras, Pytorch, Pandas, Numpy, MatplotLib).
Demonstrable experience implementing supervised models using machine/deep learning techniques, including word embeddings, convolutional and recurrent neural networks, and gradient boosting.
Knowledge and experience processing structured and unstructured data, especially Natural Language Processing (text extraction, text-based feature engineering, …).
Solid understanding of machine/deep learning concepts like backpropagation, gradient descent, vanishing gradient problem, data leakage, stratified sampling, and data augmentation.
Good applied statistics skills, such as distributions, statistical testing, regression, etc. Bayes rule, Hypothesis tests.
High level of English, written and spoken
Optional requirements
Master/Course in Data Science, Deep Learning or Machine learning (University, Udacity, Coursera…)
Python scripts for automation, unit tests, deployment
Experience with NLP python libraries like NLTK, Gensim, GluonNLP, Beautiful-Soup
Experience with state-of-the-art python libraries like Dask, Hyperas, Flair
Visualization libraries and frameworks like Seaborn, Bokeh, D3.js, GGplot, Plotly
Semi-supervised and unsupervised training, reinforcement learning
Development of custom loss functions and metrics for neural network modeling
ML-based application deployment: Web Server Gateway Interface, Flask, Django, Apache server, Docker, Kubernetes
Experience with Java, C++, PHP, Matlab/Octave
University degree or its equivalent
Web-based interfaces: Javascripts, node, angular, react
Experience with SQL and NoSQL databases
Knowledge of classical Artificial Intelligence
Experience with distributed TensorFlow (CPUs, GPUs, TPUs)
Experience with Big Data: PySpark / Scala,
Other: GIT / Linux / Bash (public repository with data science projects)
Soft Skills:
Team worker; ability to work with teams distributed geographically in different locations
Proactive, motivated and willing to consolidate and develop a professional career
Analytical, logical and critical thinking. Solid problem-solving skills: ability to identify problems and suggest mitigating and contingency actions
Expert ability to work independently and manage one’s time
Ability to deal with ambiguous situations
Practical, committed, open-minded, and positive
Advisory skills
Optional requirements
Master/Course in Data Science, Deep Learning or Machine learning (University, Udacity, Coursera…)
Python scripts for automation, unit tests, deployment
Experience with NLP python libraries like NLTK, Gensim, GluonNLP, Beautiful-Soup
Experience with state-of-the-art python libraries like Dask, Hyperas, Flair
Visualization libraries and frameworks like Seaborn, Bokeh, D3.js, GGplot, Plotly
Semi-supervised and unsupervised training, reinforcement learning
Development of custom loss functions and metrics for neural network modeling
ML-based application deployment: Web Server Gateway Interface, Flask, Django, Apache server, Docker, Kubernetes
Experience with Java, C++, PHP, Matlab/Octave
University degree or its equivalent
Web-based interfaces: Javascripts, node, angular, react
Experience with SQL and NoSQL databases
Knowledge of classical Artificial Intelligence
Experience with distributed TensorFlow (CPUs, GPUs, TPUs)
Experience with Big Data: PySpark / Scala,
Other: GIT / Linux / Bash (public repository with data science projects)
Soft Skills:
Team worker; ability to work with teams distributed geographically in different locations
Proactive, motivated and willing to consolidate and develop a professional career
Analytical, logical and critical thinking. Solid problem-solving skills: ability to identify problems and suggest mitigating and contingency actions
Expert ability to work independently and manage one’s time
Ability to deal with ambiguous situations
Practical, committed, open-minded, and positive
Advisory skills
Language requirements:
English
Level of experience (years):
Mid Career (2+ years of experience)
