Data Scientist JD

Someone with 5–7 years of experience in data collection, preprocessing and analysis of large enterprise-level data sets.

Extensive proficiency in:

Data Mining, mathematical and statistical analysis of data from various databases or data sources to drive optimization and improvement of product development, marketing techniques and business strategies.
Assess the effectiveness and accuracy of new data sources and data gathering techniques.
Develop custom data models and algorithms to apply to data sets.
Use predictive modeling to increase and optimize customer experiences, revenue generation, ad targeting and other business outcomes.
Coordinate with different functional teams to implement models and monitor outcomes.
Develop processes and tools to monitor and analyze model performance and data accuracy.
Strong problem-solving skills with an emphasis on product development.

Experience in:

Using statistical computer languages (R, Python, SLQ, etc.) to manipulate data and draw insights from large data sets.
Working with and creating data architectures.
Knowledge of a variety of machine learning techniques (clustering, decision tree learning, artificial neural networks, etc.) and their real-world advantages/drawbacks.
Knowledge of advanced statistical techniques and concepts (regression, properties of distributions, statistical tests and proper usage, etc.) and experience with applications.
Excellent written and verbal communication skills for coordinating across teams.
A drive to learn and master new technologies and techniques.

Skills preferred:

Coding knowledge and experience with several languages: Python, C, C++, Java, JavaScript, etc.
Knowledge and experience in statistical and data mining techniques: GLM/Regression, Random Forest, Boosting, Trees, text mining, social network analysis, etc.
Experience querying databases and using statistical computer languages: R, Python, SLQ, etc.
Experience using web services: Redshift, S3, Spark, DigitalOcean, etc.
Experience creating and using advanced machine learning algorithms and statistics: regression, simulation, scenario analysis, modeling, clustering, decision trees, neural networks, etc.
Experience analyzing data from 3rd party providers: Google Analytics, Site Catalyst, Coremetrics, Adwords, Crimson Hexagon, Facebook Insights, etc.
Experience with distributed data/computing tools: Map/Reduce, Hadoop, Hive, Spark, Gurobi, MySQL, etc.
Experience visualizing/presenting data for stakeholders using: Periscope, Business Objects, D3, ggplot, etc.
Excellent Communication Skills – it is incredibly important to describe findings to a technical and non-technical audience.
Strong Software Engineering Background
Hands-on experience with data science tools
Problem-solving aptitude
Analytical mind set and great business sense

Data Scientist