Join a data science team at Intuit where you’ll help conceive, code, and deploy data science models at scale. This onsite role in Mountain View, CA is designed for engineers who enjoy turning messy data into reliable ML systems, partnering with data scientists, and measuring impact through experimentation and analysis.
You’ll work within an embedded environment that focuses on practical delivery: data wrangling, model development and deployment, A/B testing and statistical analysis, and the development of agentic systems. The work also includes exploring technology shifts and evaluating how they connect to customer benefits the business aims to deliver.
Compensation: USD 140,500 - 190,000 per yearly.
What you’ll do
- Discover data sources, obtain access, import data, clean and transform it to make it machine learning ready.
- Partner with data scientists to create and refine features, and build pipelines to train and deploy models.
- Collaborate on the design, implementation, and refinement of machine learning and other algorithms.
- Run recurring A/B tests, gather data, perform statistical analysis, and draw conclusions about model impact.
- Work cross functionally with product managers, data scientists, and product engineers, and communicate results to peers and leaders.
- Explore new technology shifts and determine how they may connect to desired customer benefits.
Key requirements
- BS, MS, or PhD in Computer Science or a related field (or equivalent work experience).
- 1+ years of experience.
- Knowledge of data science tools and frameworks including Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, and Spark.
- Basic knowledge of machine learning techniques such as classification, regression, and clustering.
- Understanding of core ML principles including training and validation.
- Knowledge of data query and processing tools such as SQL.
- Computer science fundamentals: data structures, algorithms, performance complexity, and effects of computer architecture on software performance (including I/O and memory tuning).
- Software engineering fundamentals: version control and workflows (including Git and GitHub) and the ability to write production-ready code.
- Experience deploying highly scalable software supporting millions or more users.
- Experience with GPU acceleration (including CUDA and cuDNN).
- Experience integrating applications and platforms with cloud technologies (including AWS and GCP).
- Strong oral and written communication skills, including the ability to conduct meetings, make professional presentations, and explain complex technical concepts to non-technical users.
Technologies you may use
Python, Scikit, NLTK, Numpy, Pandas, TensorFlow, Keras, R, Spark, SQL, Git, GitHub, CUDA, cuDNN, AWS, GCP.
