Description:

ShyftLabs is seeking an experienced Data Scientist to improve performance and cost efficiency in their product through a deep understanding of machine learning and infrastructure systems.
The company, founded in early 2020, primarily collaborates with Fortune 500 companies to deliver digital solutions that accelerate business growth through innovation.
Responsibilities include analyzing large datasets to extract actionable insights, validating hypotheses through simulation and modeling, and developing robust experiment designs for reliable product and business decision-making.

Requirements:

A Master's degree in a quantitative discipline or equivalent is required.
Candidates must have a minimum of 5 years of relevant professional experience.
Distinctive problem-solving skills are necessary, along with the ability to articulate product questions and utilize statistics for recommendations.
Excellent verbal and written communication skills are essential for presenting information and analysis results effectively.
The ability to build positive relationships within ShyftLabs and with stakeholders, and to work effectively with cross-functional partners in a global company is required.
Strong knowledge and experience in experimental design, hypothesis testing, and various statistical analysis techniques, such as regression or linear models, is mandatory.
A deep understanding of machine learning algorithms, including deep learning, random forest, gradient boosted trees, and k-means clustering, is essential.
Experience with programming languages such as Python or R, as well as database languages like SQL or data manipulation tools like Pandas, is required.

ShyftLabs offers a competitive salary along with a strong healthcare insurance and benefits package.
The role is fully remote, allowing flexibility for employees anywhere within the United States.
The company emphasizes employee growth by providing extensive learning and development resources.
ShyftLabs is committed to creating a safe, diverse, and inclusive environment, encouraging qualified applicants from all backgrounds to apply.