Full Stack Data Scientist

Summary

Join our dynamic team as a Full Stack Data Scientist and leverage advanced analytics and cutting-edge technologies to drive impactful business outcomes. This is a remote position.

Requirements

• Bachelor's or Master's degree in Computer Science, Data Science, Statistics, or a related field
• Proven experience in data preprocessing, exploratory data analysis, and feature engineering
• Proficiency in programming languages such as Python, R, and SQL for data manipulation and analysis
• Strong understanding of machine learning algorithms and statistical modeling techniques
• Hands-on experience with machine learning libraries/frameworks such as TensorFlow, PyTorch, scikit-learn, etc
• Experience in developing and deploying end-to-end data science solutions in cloud environments (e.g., AWS, Azure, GCP)
• Solid understanding of software engineering principles and best practices for building scalable and maintainable code

Responsibilities

• Develop robust data pipelines for acquiring, cleaning, and preprocessing large-scale datasets from various sources
• Implement strategies for data quality assessment and assurance to ensure reliable analysis outcomes
• Conduct comprehensive exploratory data analysis to uncover patterns, trends, and insights within the data
• Create interactive visualizations and dashboards to effectively communicate findings to stakeholders
• Design, develop, and deploy predictive models using advanced machine learning algorithms and techniques
• Optimize model performance through feature engineering, hyperparameter tuning, and model selection
• Build scalable and efficient software solutions for deploying machine learning models into production environments
• Integrate data science workflows with existing systems and applications to enable seamless data-driven decision-making
• Establish monitoring mechanisms to track the performance of deployed models and identify opportunities for improvement
• Conduct regular maintenance activities to ensure the reliability, stability, and scalability of data science solutions
• Collaborate closely with cross-functional teams including data engineers, software developers, and business stakeholders
• Communicate technical concepts and findings effectively to both technical and non-technical audiences

Preferred Qualifications

• Experience building solutions for Commercial clients in Pharma, Biotech, CPG, Retail or Manufacturing industries
• Familiarity with containerization technologies such as Docker and orchestration tools like Kubernetes
• Knowledge of DevOps practices for continuous integration and deployment (CI/CD)
• Experience with distributed computing frameworks for parallel processing (e.g., Dask, Ray)
• Strong problem-solving skills and the ability to work effectively in a fast-paced, collaborative environment