As a Lead Data Scientist at Facet, you will push the limits of our analytics and predictive capabilities through research, experimentation, and data modeling techniques. You will join the Data Science & Analytics team within Facet tasked with modeling, understanding, and automating various aspects of our growth, margin, and retention initiatives in addition to product features. You will work with your teammates and stakeholders on both displaying the truth of the past, while doing your best to predict the unknown. You should be comfortable with communication, visualization, automation, software engineering, and reproducibility. This role is intended to wear many hats, but with a skew towards advanced & predictive analytics. You will also be involved with some reporting, data modeling, and visualization requirements.

The perfect candidate loves being both a great analyst and scientist, and hungers to improve at both.

Day-To-Day Responsibilities:

As a Lead Data Scientist with a focus on both reporting and predictive modeling, your day-to-day responsibilities will include a mix of tasks related to data analysis, visualization, and predictive model development. Your core duties will consist of:

• Exposing Insights: As a Data Scientist, your goal is not to just display data, but turn it into information. You will produce analysis reports and diagnostic models to try and discover hidden relationships and patterns between our data and metrics of interest.
• Evaluate & Produce Quality: Good code is reviewed code. You will be involved in ensuring your and your teammates’ code is free from errors, bias, and is easy to understand.
• Data Engineering: We are a newer team at a growing company, and you’ll need to do a lot of your own data engineering. Gather, clean, and preprocess data from various sources, ensuring accuracy and consistency. Perform feature engineering to generate new variables or transform existing ones to improve the quality and usefulness of the dataset. Tie all these tasks together in a pipeline and deploy on cloud based infrastructure.
• Predictive Modeling: Develop, validate, and deploy predictive models using machine learning algorithms and statistical techniques, such as regression, classification, clustering, time series forecasting, and optimization.
• Generative Modeling: Use a combination of open source and paid technologies to produce abstractions & novel features for other applications.
• Continuous Improvement: Data Science is a quickly moving field and you’ll need to keep up to date. You will need to keep abreast of the latest developments in data science, machine learning, and reporting technologies, while incorporating them into your work when appropriate. Participate in knowledge-sharing sessions to contribute to the growth and development of others.