Principal Data Scientist
As a Principal Data Scientist in Enterprise Analytics (EA), you will be responsible for leading very technically complex role-specific project work as a part of a larger cross-functional team in support of multiple lines of business. Principal Data Scientists typically analyze data and develop or apply statistical methods, machine learning models, algorithms, and infrastructure on projects that are very technically complex, requiring a deep knowledge of components of machine learning and statistical model development, algorithm design, and compute architecture. They must be able to quickly develop mastery of new subject-matter across any line of business for which they have responsibility. Our Principal Data Scientists typically work with very wide latitude for action or decisions on outcomes for projects that are typically the most complex or cross-functional in nature. They may represent EA in external conversations and typically assist with or lead the selection or oversight of outside vendor partners.
Successful candidates for this role must have very strong analytical and research skills, a deep understanding of statistics and statistical modeling, a strong understanding of modern machine learning and its mathematical underpinnings, and they must have strong programming skills with SQL, R and/or Python, and the capability to learn new programming languages as needed. They will need to be fast learners with a keen eye for detail, systems thinking, and process design. They must be team players who work steadfastly to create impactful change. This is a professional track role.
The person who fills this role will be expected to do the following as a part of their regular work responsibilities:
- Analyzes data and applies or develops models, algorithms, and/or simulations to solve complex business or technical problems, which may include assisting in building algorithms that power data products.
- Leverages a full menu of pre-defined cloud compute resources and technology and works with relevant IT teams to define and prototype new compute infrastructure, as needed.
- Performs statistical analysis to establish causal relationships between drivers and outcomes.
- Designs and implements analytic workflows using patterned cloud-based deployment architectures and works with IT teams to define and prototype new deployment architectures, as needed.
- Acts as an individual contributor in some project engagements and is responsible for implementing data science best practice in their work.
- May also act as technical project lead for major EA MLAM capabilities development projects.
- Develops and manages ETL pipes in support of their specific analytic project work.
- Develops and manages models, workflows, and/or code in support of their specific project work.
- Owns and is accountable for model and code quality and documentation for the project work they execute and establishes expectations for model and code quality and review generally.
- Supports Analytic Translators in their work to communicate and present findings and to deliver training to business partners and key stakeholders, as needed.
- Performs ad-hoc analysis as necessary to support project work.
- Helps drive projects to success, enables others to own the project's impact.
- Has very strong analytical and research skills.
- Has a deep understanding of statistics and statistical modeling.
- Has strong understanding of modern machine learning and its mathematical underpinnings.
- Has strong programming skills in SQL, R and/or Python, and the capability to learn new programming languages as needed .
- Has the skill to partner with cross-functional teams using strong written and verbal communication.
- Is a fast learner and a proven problem solver.
- Has a keen eye for system thinking and process design, especially with respect to scalability and automation.
- Has a keen eye for detail and thoughtful investigation of data.
- Has a steadfast focus on creating impactful change.
- 5-8 years of experience.
- Has some experience using the AWS big data technology stack.
- Has some experience developing production ready machine learning or advanced statistical models, particularly in the fields of NLP/text analytics or computer vision.
- Ph.D. in a Quantitative or Technical Degree Program and 3.2 GPA.
Minimum Years of Experience
Required Level of Education
Preferred Level of Education
Master's Degree in Quantitative or Technical Degree Program
Minimum GPA (4.0 Scale)
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