Lead Data Scientist - W2

OPT-EAD Candidates are NOT ELIGIBLE to be submitted for this role****

Terms of Employment

• W2 Contract, 12 months

• This position is remote.

Overview

Our client is seeking a Lead Data Scientist to lead the proliferation of machine learning and artificial intelligence throughout the enterprise. As the Lead Data Scientist, you will identify and solve business problems by using various numerical techniques, algorithms, and models in statistical modeling, machine learning, operations research, and data mining. You will use advanced analytical capabilities to support data science initiatives. The role will require communication across product teams and with customers and providing education on artificial intelligence, machine learning, and statistical models. You will lead interactions between analytics, business units and other departments.

Responsibilities

• Leads all data mining and extraction activities and applies algorithms to derive insights.

• Synthesizes analytical findings for consumption by the teams and senior executives.

• Leads proliferation of machine learning and artificial intelligence solutions.

• Applies artificial intelligence techniques to achieve concrete business goals while managing limited resources and constraints around data.

• Mentors and develops junior data scientists for advanced data analysis.

• Translates business priorities and creates data science deliverables.

• Leads implementation of ML/AI/DS best practices for new data products and builds robust and scalable software.

Required Skills & Experience

• Bachelor's Degree in Statistics, Mathematics, Computer Science or related field.

• 8 years of relevant work experience.

• In lieu of a Bachelor's degree, an additional 4 years of relevant work experience is required in addition to the required work experience.

• Ability to communicate effectively and document objectives and procedures.

• Ability to leverage a wide variety of data science tools and frameworks.

• Ability to support data exploration and data analysis tasks in support of analytics objectives.

• Knowledge in model evaluation, tuning and performance, operationalization, and scalability of scientific techniques.

• Proficiency in statistical modeling applications.

• Proficiency in advanced SQL in multiple syntaxes.