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Agent guide: [/agents.md](https://www.wearedevelopers.com/agents.md). --- # Full Stack Data Scientist - **Company:** All Views LLC - **Location:** Arlington, VA, United States (Remote available) - **Contract:** Permanent contract - **Skills:** Clean Code Principles, Amazon Web Services, Data Analysis, Microsoft Azure, Continuous Integration, Data Cleansing, Data Transformation, DevOps, Distributed Computing Environment, R (Programming Language), Python (Programming Language), Machine Learning, Power BI, Tensorflow, Software Engineering, Software Systems, SQL Databases, Tableau (Software), Workflow Management Systems, Data Processing, Google Cloud, Cloud Platform System, Feature Engineering, Pytorch, Deep Learning, Model Validation, Parallel Computation, Containerization, Scikit Learn, Kubernetes, Information Technology, Performance Monitor, Qlikview, Dask, Data Pipelines, Software Library, Docker - **Published:** July 13, 2026 - **Apply:** https://www.careerjet.com/jobad/us98a568fcdf28629a248c7ffcbfab6d2c ## About the Role We are seeking a highly skilled and innovative Full Stack Data Scientist to join our dynamic team. The ideal candidate will possess a strong background in both data science and software engineering, with a focus on developing end-to-end data-driven solutions. This role offers an exciting opportunity to leverage advanced analytics and cutting-edge technologies to drive impactful business outcomes. This is a Remote position., * 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. * Expert-level skills in data visualization platforms (e.g. Qlik, Tableau, Power BI) * 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., * 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. ## Description * Data Collection and Preprocessing: * 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. * Exploratory Data Analysis and Visualization: * 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. * Machine Learning Model Development: * Design, develop, and deploy predictive models using advanced machine learning algorithms and techniques. * Optimize model performance through feature engineering, hyperparameter tuning, and model selection. * Software Development and Integration: * 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. * Performance Monitoring and Maintenance: * 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. * Collaboration and Cross-functional Communication: * 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. ## Related Videos - [Data Science in Retail](https://www.wearedevelopers.com/videos/586-data-science-in-retail) - [From DevOps to Scaled DevOps: How We’re Rebuilding Continuous Delivery as a Platform](https://www.wearedevelopers.com/videos/100018-from-devops-to-scaled-devops-how-we-re-rebuilding-continuous-delivery-as-a-platform) - [Docker Compose: Rediscovered](https://www.wearedevelopers.com/videos/1978-docker-compose-rediscovered) - [Industrializing your Data Science capabilities](https://www.wearedevelopers.com/videos/178-industrializing-your-data-science-capabilities) - [DevOps Maturity Check – a way to balance autonomy and alignment](https://www.wearedevelopers.com/videos/58-devops-maturity-check-a-way-to-balance-autonomy-and-alignment) - [Docker build without Docker](https://www.wearedevelopers.com/videos/100114-docker-build-without-docker) ## Related Articles - [Highest Paying Tech Companies for Developers](https://www.wearedevelopers.com/magazine/220-highest-paying-tech-companies-for-developers) - [Fully Remote Software Engineer Jobs](https://www.wearedevelopers.com/magazine/447-fully-remote-software-engineer-jobs) - [Fullstack Developer Salary UK](https://www.wearedevelopers.com/magazine/251-fullstack-developer-salary-uk) - [Coffee with Developers - Maria Apazoglou - Making AI understandable for all in production](https://www.wearedevelopers.com/magazine/475-coffee-with-developers-maria-apazoglou-making-ai-understandable-for-all-in-production) - [Making Data Warehouses Fast: A Developer’s Story](https://www.wearedevelopers.com/magazine/107-making-data-warehouses-fast-a-developer-s-story) - [Top Big Data Technologies That You Need to Know](https://www.wearedevelopers.com/magazine/108-top-big-data-technologies-that-you-need-to-know)