Data Scientist II

Key Skills

Unix Operating system, Java Programming, Analytical and Problem solving, Design, Python Programming, SQL, Scrum Agile Methodology, Apache Hadoop, Data science techniques, JIRA, Data Modeling, Teradata, SAS

Job Description

Data Scientist II to discover the information advantage in vast amounts of proprietary data. For this position, we want someone with outstanding data wrangling skills and good knowledge of end-to-end modeling process. The candidate does not need experience with machine learning. 
You will work with business experts and technical partners to provide clean, well-structured data for modeling. You will use statistical analysis, graph theory and modeling techniques. This position may build models to support data curation, feature building and testing processes. As a Data Scientist II, you will lead through personal influence across multiple disciplines, make recommendations and independent decisions.

Key Responsibilities:

• Learn financial products and how data represent BAML interactions with customers and clients
• Prepare data for analytics: clean, design, move, and test huge data stores (files, tables on Teradata or Hadoop or UNIX file system)
• Research opportunities for data acquisition and discover new uses for existing data
• Provide data that are friendly, trustworthy and high quality 
• Employ a variety of languages and tools to marry systems together 
• Perform tasks of data wrangling and feature engineering
• Recommend ways to improve data reliability, efficiency and quality

Required Business Skills:

• Creative Problem-Solving: Approach data and modeling challenges with a clear eye on what is important; employ the right approach/methods to maximize time and human resources.
• Effective Collaboration: Carefully listen to business partners, analysts, and other modelers to establish requirements and ensure proper representation of information for models.
• Discipline and Organization: Apply structure and order to communication and documentation for data products and Model Management. Minimize risk by following best practices.
• Listen and Learn: Listen to experts, have a curious and exploring nature, continually push self to learn, receive constructive feedback well.

Required Technical Skills:

• Degree demonstrating training and experience in data science fundamentals including mathematical component (e.g., Computer Science, Statistics, Mathematics, Data Science)
• 2+ years of related work experience, such as deep analytics of large data, exploratory modeling 
• Strong SQL skills are necessary
• Basic applied statistics skills, such as distributions, statistical testing, feature engineering
• Able to demonstrate basic understanding of modeling algorithms

Additional Helpful Skills:

• Accessing data on Hadoop
• Agile Methodology and Artifacts, JIRA, Confluence content creation
• Bitbucket (or other version control)
• Python and/or SAS Programming