Identify the most effective architectures and models to solve complex data problems.
Work with multiple stakeholders to deliver automated workflows in a timely fashion.
Define new techniques and best practices.
Document process, procedures, and training materials for staff and stakeholders.
Attend daily stand-up and periodic team meetings.
Analyze results for robustness, validity, and out of sample stability.
Perform statistical analysis and fine-tuning using test results.state-of-the-art techniques.
Bachelor Degree in Statistics, Computer Science, or in an Engineering or Sciences discipline or related degree
At least 4 years experience in analytics, data science, ML Engineering, or software engineering.
At least 2 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.
Strong understanding of Unsupervised machine learning techniques and algorithms, 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.
Have a good knowledge of dealing with NLP.
Strong understanding of Statistics and Probability.
Data Visualization, Info-Graphics, Excel, pandas, Seaborn.
Understanding of distributed systems, Multi-GPU processing and concurrent programming.
Selecting features, building, and optimizing classifiers using machine/deep learning techniques using state-of-the-art techniques.
Extensive Python experience required.
Familiarity with Linux/UNIX operating systems, including command-line.
Experience with Agile tools, Jira & TFS.
Able to communicate technical issues with technical & non-technical stakeholders.