Data Scientist

Data Scientist

Location: Camden, NJ - Hybrid (3 days/week onsite) only locals

Duration: Long-Term

About the Role

The Data Scientist plays a pivotal role in planning, executing, and delivering machine learning-based projects that drive business impact. This role involves analyzing large datasets, developing AI/ML/optimization models, and translating findings into actionable insights. The Data Scientist partners with business and operational leaders, supports senior leadership with analytics, and fosters a culture of data-driven decision-making across the organization.

Key Responsibilities

• Collect, clean, and analyze datasets from diverse internal and external sources, applying advanced data wrangling techniques to handle structured, semi-structured, and unstructured data while ensuring completeness, consistency, and accuracy.
• Acquire access to various databases and source systems (SQL, NoSQL, graph databases) and create data pipelines for efficient and repeatable data science projects.
• Apply statistical analysis and visualization techniques (hierarchical clustering, principal components analysis (PCA)) to explore and prepare data.
• Design, develop, and validate machine learning, statistical, and optimization models for classification, regression, clustering, recommendation, and prediction tasks.
• Select appropriate algorithms and models for AI/ML, and rigorously test them for accuracy, robustness, and fairness.
• Perform feature selection and engineering, create predictive variables, and experiment with transformations to enhance performance and interpretability.
• Integrate domain knowledge into ML solutions (e.g., care delivery, financial risk, customer journey, quality prediction, sales, marketing).
• Conduct controlled experiments (A/B and multivariate testing), to evaluate hypotheses, measure workflow changes, and quantify the impact of AI solutions on operations.
• Collaborate with MLOps, data engineers, and IT to evaluate deployment options, and establish best practices around ML production infrastructure.
• Continuously monitor execution and health of production ML models, recalibrating as needed and updating them to reflect new data or changing business conditions.
• Work with cross-functional teams, collaborating with stakeholders to refine objectives, and ensure alignment between technical outputs and strategic goals.
• Create dashboards, and interactive visualizations that communicate results to a wide range of audiences, turning technical findings into actionable recommendations.
• Communicate complex projects, models, and results to diverse audiences, including executives and frontline staff, using storytelling and presentation techniques.
• Stay current with industry research and emerging technologies in AI, machine learning, and optimization, proactively experimenting with new methods and recommending adoption of tools that strengthen analytics capabilities.
• Mentor junior data scientists and analysts, provide guidance on technical approaches and model interpretation, and promote collaboration across teams.

Qualifications

• Master’s, or PhD in Computer Science, Data Science, Engineering, Statistics, Applied Mathematics, Operations Research, or a related quantitative field.
• Specialization in ML, AI, cognitive science, or data science is highly preferred.