n this role, you’ll be part of a vibrant team of Data Scientists and Machine Learning engineers.
You’ll be expected to help architect, code, optimize, and deploy Machine Learning models at scale using the latest industry tools and techniques.
You’ll also help automate, deliver, monitor, and improve Machine Learning solutions. Important skills include software development, systems engineering, data wrangling, feature engineering, architecting, and testing
BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
Strong knowledge of
Computer science fundamentals: data structures, algorithms, performance complexity, and implications of computer architecture on software performance (e.g., I/O and memory tuning).
Software engineering fundamentals: version control systems (i.e. Git, Github) and workflows, and ability to write production-ready code.
Machine Learning/Data Science languages, tools, and frameworks (e.g., Scala, Spark, Python, R, SQL, SkLearn, NLTK, Numpy, Pandas, TensorFlow, Keras, Java).
Machine learning techniques (e.g., classification, regression, and clustering) and principles (e.g., training, validation, and testing).
Data query and data processing tools/systems (e.g., relational, NoSQL, stream processing).
Distributed computing systems and related technologies (e.g., Spark, Hive).
Mathematics fundamentals: linear algebra, calculus, probability
Preferred Additional Qualifications:
Cloud technologies, in particular AWS.
DevOps concepts (e.g., CICD).
Software container technology (e.g., Docker)
Experience with designing and developing deep learning architectures.
Deploying highly scalable software for SaaS products.