Divergent
Data Scientist Internship
Job Description
Divergent is on a mission to democratize the design and manufacture of vehicles. We want to enable a future where small entrepreneurial teams across the globe will bring increasingly material and energy efficient transportation solutions to market with a fraction of the capital and time.
Divergent has invented a complete manufacturing solution to address system level challenges. The Divergent Adaptive Production System (DAPS) is a complete software hardware solution designed to replace traditional vehicle manufacturing. To make the complex simple, it is a complete modular digital factory for complex structures. Given a set of digital requirements as input, the machine automatically computationally engineers, additively manufactures, and assembles any complex structure. The system is able to move seamlessly between manufacturing different vehicle models.
Purpose of Job: We are seeking a passionate and motivated Data Scientist Intern to join our dynamic Business Intelligence team. The Business Intelligence team is an internal data team focused on delivering innovative data solutions across various business domains. We aim to turn data into actionable insights, enabling smarter internal workflows, and contributing significantly to the operational excellence of the company. The ideal candidate will have a passion for data, technology, and machine learning, with a willingness to learn and contribute to our ongoing projects. This internship offers the opportunity to gain hands-on experience in data engineering and to contribute to exciting machine learning initiatives. Responsibilities and Duties:Collaborate with the team to support the maintenance, synchronization, wrangling, and migration of data across a hybrid environment.Support the development and deployment of machine learning models.Contribute to the data exploration and analysis.Assist in designing, developing, optimizing, and maintaining data architecture and pipelines to meet data engineering principles and business objectives.Position Requirements:Familiarity with programming languages such as Python, R, Scala, DAX or MUnderstanding of databases (Big Query, Snowflake, other Relational/Non-relational), SQL, and data structures.Exposure to Power BI, Tableau, Qlikview or other BI toolsExposure to data science methodologies and machine learning algorithms.Strong analytical and problem-solving skills.Effective communication and teamwork abilities.Detailed oriented/self-motivated with the ability to learn and deploy new technology quickly.Strong work ethic and a commitment to delivering high-quality work.Ability to work independently and as part of a team.Divergent is an equal opportunity employer committed to diversity in the workplace. Our positions adhere to all statutory and regulatory requirements. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state or local laws.
No agencies, no solicitations, and no calls please. Applications will be accepted until position is filled.
Divergent has invented a complete manufacturing solution to address system level challenges. The Divergent Adaptive Production System (DAPS) is a complete software hardware solution designed to replace traditional vehicle manufacturing. To make the complex simple, it is a complete modular digital factory for complex structures. Given a set of digital requirements as input, the machine automatically computationally engineers, additively manufactures, and assembles any complex structure. The system is able to move seamlessly between manufacturing different vehicle models.
Purpose of Job: We are seeking a passionate and motivated Data Scientist Intern to join our dynamic Business Intelligence team. The Business Intelligence team is an internal data team focused on delivering innovative data solutions across various business domains. We aim to turn data into actionable insights, enabling smarter internal workflows, and contributing significantly to the operational excellence of the company. The ideal candidate will have a passion for data, technology, and machine learning, with a willingness to learn and contribute to our ongoing projects. This internship offers the opportunity to gain hands-on experience in data engineering and to contribute to exciting machine learning initiatives. Responsibilities and Duties:Collaborate with the team to support the maintenance, synchronization, wrangling, and migration of data across a hybrid environment.Support the development and deployment of machine learning models.Contribute to the data exploration and analysis.Assist in designing, developing, optimizing, and maintaining data architecture and pipelines to meet data engineering principles and business objectives.Position Requirements:Familiarity with programming languages such as Python, R, Scala, DAX or MUnderstanding of databases (Big Query, Snowflake, other Relational/Non-relational), SQL, and data structures.Exposure to Power BI, Tableau, Qlikview or other BI toolsExposure to data science methodologies and machine learning algorithms.Strong analytical and problem-solving skills.Effective communication and teamwork abilities.Detailed oriented/self-motivated with the ability to learn and deploy new technology quickly.Strong work ethic and a commitment to delivering high-quality work.Ability to work independently and as part of a team.Divergent is an equal opportunity employer committed to diversity in the workplace. Our positions adhere to all statutory and regulatory requirements. All qualified applicants will receive consideration for employment without regard to race, color, religion, sex, sexual orientation, age, national origin, disability, protected veteran status, gender identity or any other factor protected by applicable federal, state or local laws.
No agencies, no solicitations, and no calls please. Applications will be accepted until position is filled.
