Yext makes software and tools to improve everyone’s experience on the internet by providing perfect answers everywhere. Our clients trust us to use natural language processing (NLP) and machine learning (ML) to deliver correct information to end users everywhere.

As a Senior Data Scientist you will shape algorithms to develop essential new capabilities and features for Yext’s products. We manage large amounts of structured master data on behalf of some of the world’s largest companies and collect transactional data through end-user-facing applications we host directly for these clients.

Yext clients cover a tremendous breadth of industries. We are seeking data scientists excited to discover opportunities in data across a wide variety of business domains. You will work collaboratively with cross-team partners in software engineering, product management, and strategy. This approach will allow you to participate fully in the product development lifecycle and make pragmatic decisions about the design, training, and deployment of machine learning models driving key product features.

What You’ll Do

Solve data science problems end-to-end, starting from exploration and analysis to pragmatic implementations of high quality solutions
Effectively communicate quantitative results in a timely manner (you will be responsible for producing exceptional written and visual documentation)
Write high quality Python code (we use PyTorch, Transformers in many places)
Mentor junior team members, provide technical leadership and execution guidance
Exercise excellent technical judgment and prioritization around complexity, completeness, details and risks
Communicate data science and machine learning concepts to a variety of audiences (product managers, software engineers, executives, sales partners).
Partner with product management and engineering teams throughout the product development lifecycle

What You Have

Advanced degree in a quantitative discipline, e.g., computer science, mathematics, physics, statistics, applied mathematics, economics, or operations research
5+ years of quantitative/research experience
Autodidactic, Self directed – You are able to dive into a new unfamiliar problem domain and learn quickly in the face of adversity – You are comfortable with uncertainty and project evolution
Highly developed communication skills (writing and figures)
Experience leading cross-functional agile work streams and scaling data-driven initiatives
Multi-tasking, project management, and leadership skills.

Bonus Points

Experience in Tensorflow or PyTorch, and state-of-the-art transformer/NLP models and techniques (e.g. FastText, BERT, et al.).
NLP experience, especially with sentence embeddings.
Very experienced in Snowflake, MySQL, and Presto.
Excellent ability to solve "glue" problems (e.g. shell scripting, python environment setup, efficient programming practices)
Experience with common quantitative analysis and ML packages (in Python or R) such as Pandas, Matplotlib, ggplot, scikit-learn and scipy.
Experience with JVM languages (Java, Scala) a plus. GoLang or C++ also great!
Experience with AWS, Linux and Mac environments
Knowledge of statistics and good experimental practices (e.g. how to run a powerful A/B test and test for significance)
Knowledge of techniques in unsupervised machine learning and information retrieval and mining (e.g. DBSCAN, k-Means, indexes)

#LI-JB2

The base salary at the time of hire for this position is expected to be between $149,850 - $249,500. Pay ranges at Yext are established based on an analysis of salaries for positions with a similar level of accountability and impact in the relevant labor market. Salary levels are expected to change to reflect an employee’s job performance (results and impact) over time. Salaries at the time of hire are typically offered in the lower to middle of the above- referenced range in order to provide the opportunity to reflect performance- based increases over time. In addition to base salaries, employees at Yext are typically eligible for a comprehensive package of benefits, and successful candidates may also be eligible for equity (stock) based compensation and/or variable pay programs based on performance relative to goals and targets.