Junior Data Scientist in New York at Jobgether

This position is listed on behalf of a partner company, who manages all applications and next steps. Our partner is looking for a Junior Data Scientist based in the United States.

This role offers the opportunity to apply machine learning and data science to real-world business and product challenges. You will help identify AI and ML opportunities and turn them into practical solutions across a portfolio of products. The position combines traditional machine learning with emerging technologies such as generative AI and large language models. You will collaborate closely with product managers, engineers, and domain experts throughout the development lifecycle. Your work will contribute to production-ready models, scalable ML pipelines, and continuously improving AI capabilities. This is a fully remote role offering an environment centered on technical learning, collaboration, innovation, and meaningful business impact.

• Identify opportunities to apply artificial intelligence and machine learning across products and contribute to implementation efforts.

• Design, test, and refine prompts for generative AI and large language model applications.

• Build, evaluate, deploy, and maintain machine learning models in production environments.

• Partner with product managers, software engineers, and subject-matter experts to translate business challenges into effective ML solutions.

• Contribute to scalable machine learning pipelines, data workflows, model deployment processes, and monitoring practices.

• Apply established best practices for ML development, automation, deployment, and ongoing model performance monitoring.

• Analyze data and model outputs to evaluate effectiveness and identify opportunities for improvement.

• Clearly communicate technical findings, recommendations, and results to both technical and non-technical stakeholders.

• Stay informed about developments in machine learning research, industry practices, open-source projects, and emerging AI technologies.

• Share knowledge and contribute to consistent data science and machine learning practices across teams.

• Master’s or PhD in Mathematics, Statistics, Computer Science, or a related quantitative or technical discipline.

• For candidates with a Master’s degree, 2+ years of professional machine learning experience; for PhD candidates, 1+ years of professional ML experience.

• At least 2 years of experience building, deploying, and maintaining machine learning models in production.

• Strong analytical skills and solid knowledge of machine learning methodologies, algorithms, data engineering, and feature engineering.

• Hands-on experience with data warehouses, feature engineering, ML pipeline automation, and model monitoring.

• Strong understanding of data warehousing and ETL processes.

• High proficiency in Python and SQL.

• Ability to collaborate with engineering teams to develop scalable ML pipelines and follow established technical standards.

• Ability to work independently through ambiguous problems and take ownership with limited direction.

• Strong written and verbal communication skills, including the ability to explain technical concepts clearly to non-technical audiences.

• Demonstrated interest in staying current with ML research, technical publications, industry blogs, and open-source projects.

• Experience with AWS SageMaker is a plus.

• Expected base salary of $85,000–$102,000 annually, depending on geographic market, skills, experience, education, and other relevant factors.

• Fully remote work opportunity within the United States.

• Medical, dental, and vision coverage.

• Health Savings Account (HSA) and Flexible Spending Account (FSA) options.

• Life and AD&D insurance.

• 401(k) plan.

• Tuition reimbursement.

• Additional resources and programs supporting health, wellness, financial security, and overall well-being.

• Opportunities to work with machine learning, generative AI, and large language model technologies.

• Collaborative environment supporting professional development, knowledge sharing, and technical growth.