Our data scientists commonly have a university degree in statistics, math, data science, computer science, or economics and are required to have a wide range of technical competencies including:
– Descriptive statistics
– Machine learning
– Python / Jupyter programming/scripting
– SQL queries – Initial data analysis, data cleaning/wrangling and exploratory data analysis
– Data visualization and communication

As a distributed team, we value self-starters with a strong work ethic who communicate well across online channels.
Responsibilities for Data Scientists
● Work with project owners and stakeholders to interpret client objectives and offer data-backed solutions
● Mine and analyze data from large databases or flat files
● Assess the effectiveness and accuracy of new data sources to address client objectives
● Develop custom machine learning models across multiple applications
● Coordinate with stakeholders to implement models and monitor outcomes
● Develop processes and tools to monitor and analyze model performance, accuracy and drift
● Understand and interpret A/B testing results related to deployed models

Junior Data Scientist – Specific Requirements

As a junior data scientist, you will be primarily involved in analyzing data and reporting on data distribution and quality issues. You will utilize your analytic skills to gain deeper insights in data and communicate your findings to support various data science initiatives. You will receive direct support from other data scientists and strengthen your expertise.
A successful Junior Data Scientist has 1-3 years of experience, a working understanding of statistics, data visualization and communication, and exhibits the following skills:
● Experience with exploratory data analysis of on large data sets
● Proven ability to identify and propose solutions to various data quality issues
● Basic coding knowledge and experience in Python (Pandas and sklearn a plus)
● Experience with at least one visualization tool such as Tableau or Power BI
● Knowledge and experience describing data distributions using statistical methods
● Experience defining data required and querying databases to support defined objectives including combining data from multiple sources by grouping/aggregation to produce desired datasets
● Intermediate knowledge of SQL
● Experience visualizing/presenting data for stakeholders
● Basic experience creating and using machine learning algorithms such as: regression, classification, clustering, decision trees