Senior Data Scientist

Senior Data Scientist

San Francisco/Austin

Responsibilities

• Participate and guide all aspects of the project lifecycle from requirements gathering, data acquisition and preparation, hypothesis generation, ideation, coding, testing, and deployment of data science product
• Develop state of the art machine learning models. Iterate to push the boundaries on what problems can be solved through data science
• Conduct open-ended data exploration to evaluate and improve internal processes
• Collaborate with business partners to identify opportunities for innovation
• Collaborate with data engineers to identify data preparation/cleansing/ETL pipelines
• Creativity in formulating statistical questions to address business needs
• Document projects including business objective, data gathering, and processes, approaches, final algorithm, detailed set of results and metrics
• Work closely with engineers, product managers and other data scientists to develop algorithms, and initiate features and modeling discussions
• Mentor team members to help them elevate to the next level

Background and Experience

We welcome applicants from a variety of backgrounds and experiences. Below gives you a sense of how we're thinking about what you'll need to be successful in the role.

• BS, MS or Ph.D. in Computer Science, Statistics, Engineering, or a related quantitative field
• 4+ years of industry experience in productionizing machine learning models
• 4+ years of experience in writing production codes using programming language (Python), and scripting languages (Unix shell) as well as solid experience with git
• Mastery of Python, data wrangling libraries (Pandas, Numpy, etc.), and machine learning libraries (Scikit-learn, MLlib, Tensorflow, Keras, etc.)
• Excellent statistical intuition and knowledge of various approaches such as inference, hypothesis testing, sampling, etc
• Excellent communication skills, both written and oral; comfortable presenting findings and recommendations to decision-makers
• Excited about working with large data sets
• Deep understanding of machine learning models. Demonstrable experience applying these methods to real data problems such as classification, regression, recommendation system, time series analysis, etc
• Ability to deploy and scale models into a production environment as microservices or on AWS
• Experience with Jupyter notebook environment. Experience with text data and NLP is a big plus
• Experience with SQL

Bonus Points

• Experience with AWS ecosystem and Spark
• Experience with Flask or similar web-based services
• Experience with Deep Learning
• Experience with data warehouses, data lakes, and ETL