Loeb Enterprises invests in promising early stage startups and works with them beyond the financial investment to help them grow and flourish. Utilizing a shared services model, we have various teams- marketing, product, accounting, administrative, etc- available to assist and consult with all companies within the portfolio.
One of the shared services teams that has proven to be in high demand has been our Analytics Team. As it has grown in size it has been segmented into three main divisions- business intelligence, data science, and data engineering. As more and more of the companies have data-driven strategies and products, we are working to scale out all divisions, but most especially the data science team.
Success in the first 90 days
- Produce clean exploratory notebooks in Jupyter marked up with appropriate contextual information for each project.
- Clean and format raw data using extensive Pandas functionality and storing it in appropriate file formats for localized/distributed work.
- Visualize model metrics through the use of matplotlib/seaborn. (ie. ROC curves, error rate convergences etc).
- If not already familiar, demonstrate self-sufficiency in utilizing basic functionality of AWS S3, EC2, RDS (or other cloud platform).
- Analyze project roadmaps- request necessary resources and time adjustments based on expected work load to manage expectations
- Understand how outcomes of Data Science effort will feed into Business Intelligence reporting and the infrastructure help required from Date Engineers
Success in the first 180 days
- Commit standalone executable code to git for at least 3-4 projects and have it well documented for any member of the team to utilize and contribute.
- Create a library of reusable code that can be distributed to PCs and internal team to use
- Create serverless (AWS Lambda etc) functions that automatically update source data for modeling purposes and retrain models
- Partake in early assessment of effectiveness of projects to determine if it’s worth continuing down same path or if a pivot is needed.
- Suggest infrastructural changes where appropriate to facilitate future goals.
- (preferable)- Degree in social or natural sciences (and a deep understanding of relevant math’s)
- 2-3 years experience with Python and relevant libraries (Pandas, Matplotlib, Seaborn, sklearn, Jupyter notebooks etc)
- Familiarity with SQL and RDBMS concepts. (preferable) NoSQL experience as well.
- Experience with cloud technologies preferable, with examples of projects run on AWS, Azure, or GCP
- Curiosity- we’re looking for a detective who’s very adept with code to deconstruct a dataset and find great questions to ask
- Storytelling- ability to build a narrative using data
- Analytical thought processes- ability to get to the heart of a problem and organize a structured approach to it
- Good communication and listening skills. Presentation skills, understanding the audience.