Senior Data Scientist/Machine Learning Engineer

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Job description

General Purpose

The Senior Machine Learning Engineer solves real-world business challenges using machine learning in large scale systems, building successful models and deploying them in production. The role involves the application of machine learning and other related techniques in areas of natural language processing, predictions, and recommendations to improve user experiences and decision making.

Essential Duties/Responsibilities

• Work with our engineering and product teams, translating their requirements and applying the latest machine learning techniques to solve real business problems, improve our platform, and develop new capabilities
• Design, build, and train custom models and evaluate model performance
• Involved in deploying models to production, learn from customer feedback, and make frequent model improvements
• Work with Cloud and Data Engineers to contribute to the overall architecture and implementation of our ML infrastructure, data pipelines, inference engine(s), APIs, and products
• Staying up to date on emerging technologies and latest industry trends.
• Train and mentor other analysts and data scientists

Job Qualifications

Required Knowledge, Skills and Attributes

• Intermediate to advanced skills in Python, including experience with packages such as scikit-learn, TensorFlow, or spacy
• A passion for applying latest technologies into the development of innovative features and products
• A collaborative attitude and demonstrated team-working ability and experience working in an Agile environment
• Ability to work with limited supervision within a cross functional team and business

Required Education and/or Experience

• 5+ years of hands-on experience working with machine learning and other statistical techniques (clustering, decision tree learning, artificial neural networks, etc.)
• A Masters or Ph.D in Machine Learning or related field or 10+ years professional programming experience

Desirable Education and/or Experience

• Experience in applying machine learning for global-scale applications
• Exposure to AWS tools such as SageMaker and developing ML models in Jupyter Notebooks
• Experience with NLP, Pytorch, Keras, fast.ai
• Experience with graph-based ML or knowledge graphs