Full Stack Data Scientist

rockITdata•Philadelphia, PA

1d•Remote

About The Position

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.

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.

Nice To Haves

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.

Responsibilities

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.