Intuit is the global financial technology platform that powers prosperity for the people and communities we serve. With approximately 100 million customers worldwide using products such as TurboTax, Credit Karma, QuickBooks, and Mailchimp, we believe that everyone should have the opportunity to prosper. We never stop working to find new, innovative ways to make that possible.

Job Overview

As a Senior Staff Software Engineer at Intuit, you will play a significant role in designing and implementing our Modern analytics solutions, which is fundamental to driving our innovative solutions at scale to derive any analytical Insights.. We are looking for a technical individual with profound expertise in Java, building platforms with ML , and AWS to drive major capabilities and ensure operational excellence in high-impact projects.

Responsibilities

Lead the architecture and implementation efforts of multiple capabilities or sections within our MAP
Drive significant technology initiatives end-to-end and across multiple layers of architecture.
Drive design and implementation of durable and software solutions that will solve critical customer problems, are scalable, secure, easy to maintain, and interact with numerous other services
Serve as a technical leader and expert across multiple teams.
Drive and guide innovative work that may include the development of new-to-the-world technology solutions.
Collaborate with cross-functional teams to identify, share, and address risks impacting our business.
Drive customer-focused design and development with a deep understanding of customer pains and needs.
Mentor and assess engineers to foster a talented and inclusive technical team.
Provide recommendations and best practices for application development, platform development, and developer tools
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.
Run regular A/B tests, gather data, perform statistical analysis, draw conclusions on the impact of your models.
Work cross functionally with product managers, data scientists and product engineers, and communicate results to peers and leaders.
Explore new technology shifts

Qualifications

BS, MS, or PhD degree in Computer Science or related field, or equivalent practical experience.
Minimum 12+ years of professional experience in software engineering.
Knowledgeable with Data Science tools and frameworks (i.e. Python, Scikit, NLTK, Numpy,Pandas, TensorFlow, Keras, R, Spark).
Basic knowledge of machine learning techniques (i.e. classification, regression, and clustering).
Understand machine learning principles (training, validation, etc.)
Knowledge of data query and data processing tools (i.e. SQL)
Computer science fundamentals: data structures, algorithms, performance complexity, and
implications of computer architecture on software performance (e.g., I/O and memory tuning).
Proficiency in Spring/ Golang and extensive experience with AWS.Practical experience in developing applications using microservices, container technologies, container management systems such as Kubernetes, Mesos etc
Demonstrated ability to lead multiple scrum teams (~10 engineers).
Experience in leading technical design, architecture, and code reviews.
Strong ability to simplify technical designs and operations to enhance team understanding and performance.
Understand machine learning principles (training, validation, etc.)
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.
Mathematics fundamentals: linear algebra, calculus, probability
Experience using deep learning architectures
Experience deploying highly scalable software supporting millions or more users
Experience with GPU acceleration (i.e. CUDA and cuDNN)
Experience with integrating applications and platforms with cloud technologies (i.e. AWS and GCP)