Data Scientist - Camden NJ ( local candidates only)

Data Scientist

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

Education

• 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.

Experience and Skills

• 3-5 years of hands-on experience planning and executing end-to-end data science projects with demonstrated impact on clinical or operational outcomes in business environments
• Advanced programming proficiency in Python or R with strong expertise in machine learning frameworks (scikit-learn, TensorFlow, PyTorch) and statistical analysis tools
• Expertise in machine learning and statistical techniques including supervised/unsupervised learning, deep learning, NLP, computer vision, regression models, ensemble methods, and experimental design (A/B testing)
• Strong data engineering capabilities including SQL/NoSQL database programming, distributed computing tools (Hadoop, Spark, Kafka), data pipeline development, and experience with cloud platforms (AWS, Azure, GCP)
• Production ML and MLOps experience including model deployment, monitoring, containerization (Docker, Kubernetes), version control, and applying DevOps principles to data science workflows
• Data visualization and communication excellence with ability to create compelling dashboards (Tableau, Power BI), translate complex technical findings into actionable insights, and present to diverse audiences from executives to frontline staff
• Cross-functional collaboration skills with proven ability to work in agile environments, partner with stakeholders to align technical solutions with business objectives, and mentor junior team members
• Healthcare domain knowledge preferred, particularly experience with Epic EHR systems, clinical workflows, and healthcare data standards, along with relevant certifications (Clarity /Caboodle, Google Cloud ML Engineer, AWS ML Specialist)