Machine Learning Engineer - Computer Vision/NLP

Key Skills
 5+ years of experience in building production-grade Neural Network models using CV or NLP techniques.
 Excellent understanding of machine learning techniques and algorithms, such as k-NN, Naive Bayes, SVM, Decision Forests, Neural Networks, etc.
 Experience with Deep Learning frameworks, such as TensorFlow, PyTorch, MxNet.
 Experience with common data science toolkits, such as R, Sklearn, NumPy, MatLab, and MLib. Excellence in at least one of these is highly desirable.
 Good applied statistics skills, such as distributions, statistical testing, and regression.
 Proficiency in using query languages such as SQL, Hive, Pig, and NoSQL databases.

Responsibilities & Objectives :
 Collaborate with Product Managers, Architects, and Engineering Leadership to conceptualize, strategize, and develop new products centered around AI/ML initiatives.
 Develop, drive and execute the long-term vision and strategy for the Data Science team by working with multiple teams and stakeholders across the country.
 Architect, design, and develop large-scale machine learning systems.
 Develop Neural Network models for information extraction from mortgage documents using Computer Vision and NLP techniques.
 Perform ad-hoc analysis and present results in a clear manner to a wide audience and key stakeholders.
 Design experiments, test hypotheses, and build models.
 Conduct advanced data analysis and highly complex designs algorithm.
 Apply advanced statistical, predictive, and machine learning modeling techniques to build, maintain, and improve on multiple real-time decision systems.
 Collaborate with development teams on deploying models to the production environment to support ML-driven product features.
 Define business-specific performance metrics to measure model effectiveness. Monitor and improve metrics over time for models used in a production environment.

Qualifications:
 M.S. in mathematics, statistics or computer science or related field; Ph.D. degree preferred.
 5+ years of relevant quantitative and qualitative research and analytics experience.
 Excellent communication skills and ability to convey complex topics to a cross-functional audience.,