Artificial Intelligence....Full time ...Great job offer
The ideal candidate will combine expert AI/Client/Open Source/Tech knowledge with hands-on experience building algorithms/models/programming and outstanding business skills (revenue/cost drivers, customer experience, customer journey, communication, etc.) to manage and deliver complex/critical projects driving significant value to the client. Your primary focus will be in applying data mining techniques, doing statistical analysis, and building high-quality prediction systems integrated with our products. You will be required to use your skills in algorithm and predictive modeling to solve real-world quote to cash problems.
Responsibilities
• Selecting features, building and optimizing classifiers using machine learning techniques
• Data mining using state-of-the-art methods
• Extending the company’s data with third party sources of information when needed
• Enhancing data collection procedures to include information that is relevant for building analytic systems
• Processing, cleansing, and verifying the integrity of data used for analysis
• Doing ad-hoc analysis and presenting results in a clear manner
• Creating automated anomaly detection systems and constant tracking of its performance
Skills and Qualifications
• Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, etc.
• Experience with common data science toolkits, such as R, Weka, NumPy, MatLab, etc
• Great communication skills
• Proficiency in using query languages such as SQL is mandatory
• Experience with NoSQL databases, such as MongoDB
• Good applied statistics skills, such as distributions, statistical testing, regression, etc.
• Good scripting and programming skills with Python
• Data-oriented personality
• Understanding of Neural Networks and Natural Language Processing
Expertise required in the following areas
• Advanced knowledge in model evaluation, tuning and performance, operationalization and scalability of scientific techniques and establishing decision strategies.
• Hands-on experience developing supervised and unsupervised machine learning algorithms (regression, decision trees/random forest, neural networks, feature selection/reduction, clustering, parameter tuning, etc.),
• Experience in evaluating and making decisions around the use of new or existing methodologies, process, and tools
• Experience in working with cross-functional teams and multi-dimensional data