Data Scientist

Analytica•Bethesda, MD

14d

About The Position

Analytica is seeking a Data Scientist to lead the design and implementation of advanced statistical and AI/ML solutions supporting product safety analytics initiatives. This role focuses on extracting actionable insights, evaluating model performance, and applying semantic analysis techniques to enhance data-driven decision-making. The ideal candidate will bring an innovative mindset and experience working in modern cloud environments to improve efficiency, scalability, and interpretability of AI solutions. This work supports the strategic vision to accelerate toward an AI-powered product safety Sentinel model—protecting more consumers, faster, from more hazards by leveraging analytics to shorten time to intervention.

Requirements

Bachelor's Degree required, Master’s degree, Ph.D., preferred in Data Science, Machine Learning, Computer Science, Mathematics, or a related field
5+ years of experience developing statistical and AI/ML models using modern tools and best practices
3+ years of experience building predictive models, including regression, classification, and NLP applications
3+ years of hands-on experience with Python (e.g., Pandas, Scikit-learn, PyTorch or TensorFlow)
3+ years of experience working in cloud environments (Azure preferred)
2+ years of experience with SQL for data querying and manipulation
Strong experience with data visualization tools such as Power BI (or equivalent)
Demonstrated ability to apply semantic analysis or coding techniques within ML workflows
Strong written and verbal communication skills with the ability to convey technical concepts clearly
Experience using Git-based version control for collaborative development
Ability to work effectively within Agile teams, contributing to sprint planning, iterative development, and continuous improvement
Proven ability to collaborate across technical and business stakeholders to deliver high-quality, data-driven solutions

Responsibilities

Apply semantic coding and analysis techniques to uncover patterns and thematic insights from structured and unstructured data
Translate complex analytical findings into clear, actionable insights using data storytelling and visualization techniques
Support decision-making through development of interpretable and explainable analytics outputs
Design, implement, and maintain AI/ML models including: Product similarity models, Supervised and unsupervised learning approaches, Models for anomaly detection, classification, regression, and pattern recognition
Evaluate models using robust performance metrics (e.g., precision, recall, F1-score, AUC) to ensure accuracy, reliability, and fairness
Continuously improve model performance through testing, validation, and tuning
Explore emerging technologies and innovative methodologies to solve complex analytical challenges
Leverage modern development tools and frameworks (e.g., GitHub Copilot, CI/CD pipelines) to streamline workflows and improve delivery efficiency
Optimize data science workflows for scalability and performance in cloud environments (Azure)