Full Stack Data Scientist (AI)

TL;DR

Full Stack Data Scientist (AI/ML): Developing end-to-end data-driven solutions for healthcare and government sectors with an accent on data pipelines, predictive modeling, and production deployment. Focus on building scalable software for ML model integration and optimizing performance through feature engineering and hyperparameter tuning.

Location: Remote (US)

Company

A services company specializing in AI, automation, and data transformation for commercial healthcare, life sciences, and government agencies.

What you will do

• Develop robust data pipelines for acquiring, cleaning, and preprocessing large-scale datasets.
• Conduct comprehensive exploratory data analysis and create interactive visualizations using Qlik, Tableau, or Power BI.
• Design, develop, and deploy predictive models using advanced machine learning algorithms.
• Build scalable software solutions to integrate ML workflows into production environments.
• Establish monitoring mechanisms to track deployed model performance and ensure stability.
• Collaborate with data engineers, software developers, and business stakeholders.

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field.
• Proven experience in data preprocessing, EDA, and feature engineering.
• Expert-level skills in data visualization platforms (e.g., Qlik, Tableau, Power BI).
• Proficiency in Python, R, and SQL for data manipulation.
• Hands-on experience with ML frameworks such as TensorFlow, PyTorch, or scikit-learn.
• Experience deploying end-to-end data science solutions in cloud environments (AWS, Azure, GCP).

Nice to have

• Experience building solutions for Pharma, Biotech, CPG, Retail, or Manufacturing industries.
• Familiarity with Docker, Kubernetes, and CI/CD DevOps practices.
• Experience with distributed computing frameworks like Dask or Ray.

Culture & Benefits

• Entrepreneurial work environment that encourages thinking differently.
• Culture based on 10 Guiding Principles, including transparency and "progress, not perfection".
• Fast-paced and challenging atmosphere where no two days are the same.
• Commitment to equal opportunity and non-discrimination in employment practices.