Junior Data Scientist

Accountabilities:

• • 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.

  Requirements:

  • 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.

  Benefits:

  • 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.