Machine Learning Engineer - Computer Vision/NLP

You have over 5 years of experience in building production-grade Neural Network models using Computer Vision or Natural Language Processing techniques. You possess a strong understanding of various machine learning techniques and algorithms, including k-NN, Naive Bayes, SVM, Decision Forests, and Neural Networks. Experience with Deep Learning frameworks like TensorFlow, PyTorch, and MxNet is part of your skillset. Proficiency in common data science toolkits such as R, Sklearn, NumPy, MatLab, and MLib is highly desirable. Additionally, you have solid applied statistics skills encompassing distributions, statistical testing, and regression. Your expertise extends to using query languages like SQL, Hive, Pig, and NoSQL databases. In this role, you will collaborate with Product Managers, Architects, and Engineering Leadership to conceptualize, strategize, and develop new products focused on AI/ML initiatives. You will be responsible for developing, driving, and executing the long-term vision and strategy for the Data Science team by engaging with multiple teams and stakeholders across the organization. Your tasks will involve architecting, designing, and implementing large-scale machine learning systems. Specifically, you will develop Neural Network models for information extraction from mortgage documents using Computer Vision and NLP techniques. Ad-hoc analysis and clear presentation of results to various audiences and key stakeholders will be part of your routine. You will also design experiments, test hypotheses, and build models while conducting advanced data analysis and highly complex algorithm designs. Applying advanced statistical, predictive, and machine learning modeling techniques to enhance multiple real-time decision systems will be a key aspect of your role. You will collaborate with development teams to deploy models in the production environment to support ML-driven product features. Furthermore, you will define business-specific performance metrics to assess model effectiveness and continuously monitor and enhance these metrics over time for models in a production environment. To qualify for this position, you should hold an M.S. in mathematics, statistics, computer science, or a related field, with a Ph.D. degree being preferred. You must have over 5 years of relevant quantitative and qualitative research and analytics experience. Excellent communication skills and the ability to effectively convey complex topics to a diverse audience are essential attributes for this role.,