Data Scientist at Even Financial
Some of the Questions We Explore Include:
- How can we rank financial product offers for each consumer we see given a point in time need?
- How can we integrate with third party data sources to enhance our recommendation models?
- How can we best mix active and passive signals to maximize click through rates?
- How do we optimize against the different data flows we have, given our open API?
- How do we build a strong machine learning infrastructure that automatically trains, experiments with and monitors model performance?
Typical Day to Day Responsibilities Includes:
- Work on different elements of Learning to Rank problems, such as constructing ranking experiments and measuring their results
- Analyze, develop, test, and introduce new features and signals for our ranking and filtering systems via analysis of historical consumer data, third party data sources, and various other ML techniques
- Work with the team to build machine learning infrastructure to streamline the ways in which we experiment with or analyze our data, and partner with cross functional teams (partner solutions, engineering, product, business intelligence) to roll data science outputs into production
- Build comprehensive dashboarding and simulation systems to test models offline before they are released to production, as well as to monitor their performance once they are online
- Produce documentation that captures experiments or analyses in detail across the entire data science lifecycle (research to results)
- Develop, document, and implement processes to help the team to scale
- Work on ad hoc analyses related to business requirements
- Collaborate with teammates in an agile, high velocity environment with a focus on meeting business needs
- Work with languages such as Scala, Python, SQL and have familiarity with AWS redshift or similar data warehousing.
- Business acumen and communication skills (ability to communicate business value and impact), creative problem-solving and intellectual curiosity
- Ability to work well in a team and openly collaborate with a focus on executing fast
- Ability to decompose large, complex problems into smaller actionable parts
- Experience with pragmatic statistical analysis in a workplace setting
- Experience with data science models and testing and building them from scratch (e.g. logistic, tree-based models)
- High proficiency in python and SQLBA/BS in Computer Science, Math/Finance, Physics, Applied Economics, Statistics or other technical field preferred or relevant experience (3+ years)
- Experience analyzing and computing data at scale with tools such as Spark, MapReduce, Hive, Presto, Redshift, etc. preferred but not required.
- A sense of humor and a genuine interest in joining a fun, dynamic environment where we are building things from the ground up!