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.