Lead Growth Data Scientist

FairSquare•San Diego, CA

6d•$150,000 - $170,000•Hybrid

About The Position

A Growth Data Scientist is responsible for overseeing the collection, storage, and interpretation of data for the business in order to provide meaningful insights about how to improve business operations and performance. General duties include partnering with organizational and department leaders to identify key data points, align data definitions, create organized data repositories, compare data points to current company processes, and write reports detailing business behavior, predictions and/or proposals. Data Scientists apply data science techniques across various domains, such as marketing, credit risk, and sales.

Requirements

5+ years' experience of equivalent combination of education and experience.
Bachelor's degree in a quantitative field such as statistics, mathematics, computer science, engineering, or related discipline required.
Experience mentoring others data scientists.
Experience working with building, deploying, and maintaining recommendation systems and models in a live environment.
Fluency in programming languages such as R, Python, SQL.
Understanding and application of supervised and unsupervised machine learning algorithms and statistical analysis methods.
Proficiency in working with SQL databases.
Strong programming practices utilizing version control systems (e.g. Git).
Demonstrated machine learning experience.
Attention to detail.
Strong communication skills across technical and non-technical audiences.

Nice To Haves

Experience with cloud technology (AWS) a plus.
Experience in FinTech/Credit space a plus.
Experience with Tableau and/or Salesforce preferred.

Responsibilities

Engage stakeholders to understand business problems and requirements;
Manipulate structured and unstructured datasets from disparate data sources;
Perform data preprocessing, integration, cleansing, descriptive analysis and visualization;
Perform exploratory data analysis, ad-hoc analysis, and root cause analysis; identify data issues and communicate with data owners;
Develop models and algorithms to automate and improve business decisions;
Identify and test hypotheses, ensuring statistical significance, as part of building predictive models for business application;
Monitor and analyze model performance for validation and troubleshooting of issues that may arise;
Provide consultation to business leaders and other stakeholders on how to leverage analytics insights and build strategies around analytics;
Participate in R&D work to further advance the analytics platform;
Contribute to improving the overall analytic capabilities of the team; and
Other duties as assigned.