Data Scientist

Hyperspace Ventures • Full-time • Remote (United States) • $110.54k / year • 7h ago

About this role

We're looking for Data Scientists to work on our core and business products to help shape the future of what we build at Hyperspace Ventures. You will enjoy working with various data sets, cutting edge technology, and the ability to see your insights turned into real products on a regular basis. The perfect candidate will have a background in a quantitative or technical field, will have experience working with large data sets, and will have some experience in data-driven decision making. You are focused on results, a self-starter, and have demonstrated success in using analytics to drive the understanding, growth, and success of a product.

Qualifications for this role

2+ years experience doing quantitative analysis
BA/BS in Computer Science, Math, Physics, Engineering, Statistics or other technical field
Experience in SQL or other programming languages
Development experience in any scripting language (PHP, Python, Perl, etc.)
Experience communicating the results of analyses with product and leadership teams to influence the strategy of the product
Knowledge of statistics (e.g., hypothesis testing, regressions)
Experience manipulating data sets through statistical software (ex. R, SAS) or other methods

Preferred Qualifications:

Advanced degrees
Experience with distributed computing (Hive/Hadoop)

Responsibilities

Apply your expertise in quantitative analysis, data mining, and the presentation of data to see beyond the numbers and understand how our users interact with both our consumer and business products
Partner with Product and Engineering teams to solve problems and identify trends and opportunities
Inform, influence, support, and execute our product decisions and product launches
The Data Scientist Analytics role has work across the following four areas:

Product Operations
Forecasting and setting product team goals
Designing and evaluating experiments
Monitoring key product metrics, understanding root causes of changes in metrics
Building and analyzing dashboards and reports
Building key data sets to empower operational and exploratory analysis
Evaluating and defining metrics

Exploratory Analysis
Proposing what to build in the next roadmap
Understanding ecosystems, user behaviors, and long-term trends
Identifying new levers to help move key metrics
Building models of user behaviors for analysis or to power production systems

Product Leadership
Influencing product teams through presentation of data-based recommendations
Communicating state of business, experiment results, etc. to product teams
Spreading best practices to analytics and product teams

Data Infrastructure
Working in Hadoop and Hive primarily, sometimes MySQL, Oracle, and Vertica
Automating analyses and authoring pipelines via SQL and python based ETL framework

Benefits