Tech Lead, Machine Learning Evaluation at McD Tech Labs (Mountain View, CA)

We are currently looking for a data science leader to establish and lead our ML evaluation team at McD Tech Labs. This role reports directly to the Head of Data for McD Tech Labs. This leader will be tasked with defining how we evaluate quality of our machine learning algorithms and models that operate with petabyte-scale worth of customer voice ordering data. What evaluation data sets make sense? How do we craft and curate such data sets? What data mining techniques and systems are necessary to identify such data across diverse regions and stores worldwide?

The ideal candidate has experience leading data scientists and engineers focused on performance evaluation and optimization of machine learning products, and a passion for building agile, high-performing teams.

Responsibilities:

• Build, lead and develop a team of data scientists in a fast-moving and quickly growing organization

• Collaborate with technology, engineering, and other data science team leaders to define success of machine learning software development lifecycle

• Define, collect, curate and own overall ML evaluation datasets, methodology, process and frameworks, covering ASR, Dialog, NLU models and systems.

• Design test cases, automate regression suites and end-to-end evaluation system that scale for ML models operating at petabyte scale.

• Employ data mining and statistical techniques to tackle diverse and large-scale ML data challenges.

• Provide cross-functional teams and leadership with regular updates and strategic guidance regarding ML model quality through evaluation test framework

Qualifications:

• Bachelor’s in Computer Science, Mathematics, Statistics or a related quantitative field

• 5+ years of experience in data science with proven experience leading a team of Data Scientists

• 3+ years of Python experience working with big data

• Strong understanding of high-performance algorithms, and data mining

• Experience in developing state-of-the-art data science techniques for large-scale machine learning products

• Strong understanding of ML data pipelines from defining, collecting to curating ground truth and evaluation data sets for ML models

• Demonstrated ability to facilitate and coordinate complex data design and development activities across teams in agile sprints with minimal direction