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Über
This role is 100% remote, however, the candidate will be asked to come into the office 10-12 days out of the year. This is a 12-month contract. Identifies and solves business problems by using various numerical techniques, algorithms, and models in statistical modeling, machine learning, operations research, and data mining. Uses advanced analytical capabilities to support data science initiatives. Communicates across product teams and with customers and educates on artificial intelligence, machine learning, and statistical models. Acting as a liaison and interface between analytics, business units and other departments. Essential functions include: Leads data mining and extraction activities and applies algorithms to derive insights. Uses data to understand the data profile and its limitations and applies it to business solutions. Collaborates with business units to innovate organizational performance. Explores data patterns using existing data science tools. Translates business priorities to data science deliverables. Implements new data products and builds robust and scalable software. Leads/Guides DS on implementing best practices for projects and applies artificial intelligence techniques to achieve concrete business goals while managing limited resources and constraints around data. Qualifications include: 5+ years of experience working as a Data Scientist in AI/ML Experience performing end-to-end deployments and migrations in ML Experience working with Python and AWS SageMaker Experience working with Bigdata technologies Education level: Bachelor's Degree in Statistics, Mathematics, Computer Science, or related field. In lieu of a Bachelor's degree, an additional 4 years of relevant work experience is required in addition to the required work experience. Preferred qualifications include: Knowledge, skills, and abilities (KSAs) Ability to work independently without detailed guidance. Ability to communicate effectively and document objectives and procedures. Ability to leverage a variety of data science tools and frameworks. Knowledge in model evaluation, tuning and performance, operationalization, and scalability of scientific techniques. Knowledge of software engineering principles and practices. Proficiency in statistical modeling applications. Proficiency in advanced SQL in multiple syntaxes.
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