AI/Machine learning Team Lead

Requirements

• Bachelor Degree in Mathematics, Statistics, Computer Science, Physics, or in an Engineering or Sciences discipline or related degree
• At least 4 years experience in analytics, data science, ML Engineering, or software engineering- or, a Masters degree in one of the above mentioned fields and 2 years of relevant/scientific experience.
• At least 1 year of experience leading, mentoring, and developing either Machine Learning, Data Science, or Analytics practitioners.
• Strong understanding of Supervised machine learning techniques and algorithms,such as k-NN, Naive Bayes, SVM, Decision Forests, Logistic Regression, also Ensembling methods like Gboost, XGboost, LightGboost and Yandex Catboost with ability to use Stacking methods between them.
• Strong understanding of Unsupervised machine learning techniques and algo-rithms, such as K-means, DBSCAN, OPTICS, GMM and spectral clustering with the ability to evaluate models using different techniques.
• A good knowledge of using Reinforcement Learning algorithms like Q-Learning and Deep Q-learning, SARSA and montcarlo methods.
• Solid understanding of Deep learning techniques and algorithms, such as MLP,CNN, LSTM and auto-encoder techniques with the ability to use pre-trained models in CNN like Resnet50, VGG19, Exception, in addition to using state of the art object segmentation and localization algorithms like SSD, YOLO, R-CNN and faster R-CNN, also have the ability of using GANs as a generator model.
• Have a good knowledge of dealing with NLP problems like sentiment analysis, machine translation Named Entity Recognition, Part of Speech Tagging and Language models, also has a good knowledge using embedding methods starting from BOW, TF-IDF, Word2Vec, Glove, FastText, Elmo and seq2seq models with attention mechanism, also familiar with Transformer based algorithms like BERT, AlBERT, GPT-2.
• For Speech and ASR, have a good understanding in signals,FFT, spectrogram analysis, MFCC, CTC loss, Phonems, Graphems, GMM, HMM for Acoustic Model, also can deal with Language Models like KenLim and SRILM, in addition can deal with speech frameworks like Kaldi, DeepSpeech and Jasper also can use Unicode to roman translation like Buckwalter.
• Have a good knowledge of Statistics and Probability.
• Data Visualization, Info-Graphics, Excel, pandas, Seaborn.
• Working with large data and tools for data analysis, e.g., Pandas, Numpy, Seaborn, matplot, etc.
• Experience with AI cloud services providers like MS Azure, Google Cloud, IBM Watson.
• Understanding of distributed systems, Multi-GPU processing and concurrent programming.
• The ability to deal with machine learning libraries like Scikit-learn, Tensoflow, Keras, PyTorch.
• Data Cleaning, normalizing, labelling and algorithms evaluation experience
• Have the ability to work with Git, linux , Docker, and containers clustering and orchestration frameworks like Jenkins, Kubernetes, familiar with CI/CD techniques.
• Have the ability to build and deploy models on the edges using Optimized-Based Engines like Tensorflow-Lite and Intel OpenVino.
• Has the ability to packaging and deploy models as API using Flask, wsgi, nginx or falcon and deploy it on-cloud (SaaS based software).
• Selecting features, building, and optimizing classifiers using machine/deep learn-ing techniques using state-of-the-art techniques.
• Object Oriented Programming (OOP) and software design skills, can transform code to C++. Extensive Python experience would be a plus as would be experience with Reactive Programming
• Familiarity with Time-Series analysis using Hidden Markov Model (HMM) and Deep Learning (LSTM).
• Familiarity with Linux/UNIX operating systems, including command-line.
• Experience with Agile tools, Jira, Trello, Pivotal Tracker, or related
• Experience with ML tools: Keras, Jupyter Notebooks, Pandas, scikit-learn, Flask, OpenCV, Tesseract, Numpy, or similar
• Able to communicate technical issues with technical & non-technical stakeholders

Preferred Skills/Experience Nice to Have:

• Graduate degree in a mathematical, scientific, or engineering discipline
• Past involvement or exposure to authoring research papers, findings reports, or conference proceedings
• Strong mathematics and data analysis skills
• Solid understanding of data types, structures, and algorithms
• Understanding of Continuous integration, testing, deployment & release methodologies
• Version control such as GIT, Subversion, Team Foundation
• Ability to understand large scale software & system architecture