In this role, you-ll be part of a vibrant team of data scientists, product developers 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. As the Senior Engineer - you will be responsible for defining and maintaining the ML Data Architecture for Quickbooks Capital-s use-cases. 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.
- Design and build systems which improve machine learning scalability, usability, and performance.
- Work cross functionally with product managers, data scientists, and engineers to understand, implement, refine, and design machine learning and other algorithms.
- Discover data sources, get access to them, import them, clean them up, and make them machine learning ready.
- Work with data scientists to create and refine features from the underlying data and build pipelines to train and deploy models.
- Partner with data scientists to understand, implement, refine and design machine learning and other algorithms.
- Effectively communicate results to peers and leaders.
- Explore the state-of-the-art technologies and apply them to deliver customer benefits.
- Interact with a variety of data sources, working closely with peers and partners to refine features from the underlying data and build end-to-end pipelines.
- BS, MS, or PhD degree in Computer Science or a 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., Spark, Scala, 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
- Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark).
- Knowledge of data query and data processing tools (i.e. SQL)
- Experience using deep learning architectures
- Cloud technologies, in particular AWS.
- DevOps concepts (e.g., CICD), Software container technology (e.g., Kubernetes, Docker)
- Experience with designing and developing deep learning architectures
- Deploying highly scalable software for SaaS products
- Keep up with the industry trends and academia on AI, Machine Learning and state-of-the-art experimental systems
- Experience deploying highly scalable software supporting millions or more users