Principal AI Data Scientist

Job Description

Job Description

Infomatics is partnered with a large retailer that is hiring a Principal AI Data Scientist on a direct hire/FTE basis near Phoenix, AZ. Can work remote. All applicants must be eligible & willing to be hired on W2.

You will lead various AI efforts involving computer vision, deep learning, and nlp in addition to other machine learning model builds. You will not only work on large scale projects to provide value to the customers but are also routinely involved in building our internal R&D capability to have an edge in the analytics industry. You will lead some of the most strategic and very complex problems.

Duties/Responsibilities:

• Builds and validates machine learning models of high risk/reward problems utilizing large scale data from multiple data sources and methodologies.
• Uses machine learning techniques to create data-driven solutions for various business use-cases.
• Writes programs utilizing existing libraries and methodologies.
• Interprets, communicates, and presents analytic results to C-Level executives and below.
• Consistently collaborates with fellow data scientists, data engineers, business partners, project managers, cross-functional teams, key stakeholders, and other domains to drive business value.
• Leads AI best practice sharing opportunities and knowledge of industry trends and innovations in data science.
• Leads projects with external partners and vendors to develop solutions to meet business needs while resolving any issues that may arise.
• Contributes to the organization's data strategy and roadmap.
• Embeds and drives the organization with the most up-to-date AI methodology.

Qualifications:

• Master's or PhD degree in a quantitative field with 5+ years of data science experience.
• Applied expertise in artificial intelligence with experience applying natural language processing, computer vision (image processing), and deep leaning. Need to have the capability to leverage current mature mainstream AI application tools and methodology
• Proficiency in m achine l earning with familiarity and actual applications of scikit-learn library machine learning techniques such as decision tree, gradient boosting, XGBoost, etc. for regression, classification, or segmentation problems.
• Programming expertise in Python with familiarity with cloud environments (AWS, Databricks, etc.)
• Ability to work with large data sets from multiple data sources
• Ability to communicate complex analytics concepts and techniques to C-Level executives and below
• Ability to work collaboratively with other data scientists, data engineers, multiple stakeholders across the business, and with external partners
• Intellectual curiosity, a passion for data, and a results orientation.