Data Scientist

Compunnel, Inc.; Camden (NJ); On-site; USD 100,000 - 130,000; Full time

Job summary

A leading analytics firm is seeking a Data Scientist to plan and execute machine learning-driven solutions that drive business impact. Responsibilities include analyzing complex datasets, developing AI/ML models, and collaborating with technical teams. The ideal candidate has a Master’s or PhD in a quantitative field and 3–5 years of experience in data science. Strong programming skills in Python or R and expertise in machine learning frameworks are essential. This role offers the chance to work on impactful projects in an innovative environment.

Qualifications

• 3–5 years of hands-on experience in data science projects.
• Experience with MLOps practices including model deployment and monitoring.
• Healthcare domain experience is a plus.

Responsibilities

• Collect and analyze large datasets from diverse sources.
• Develop and maintain data pipelines for data science workflows.
• Design, develop, and validate machine learning and optimization models.
• Monitor and optimize production models.
• Create dashboards and visualizations for insights communication.

Skills

Python programming

R programming

Statistical analysis

Machine learning frameworks (e.g., scikit-learn, TensorFlow, PyTorch)

Data visualization (e.g., Tableau, Power BI)

SQL/NoSQL

Data pipelines and distributed computing tools (e.g., Hadoop, Spark)

Cloud platforms (e.g., AWS, Azure, Google Cloud)

Collaboration and communication

Education

Master’s or PhD in Computer Science, Data Science, Engineering, Statistics, Applied Mathematics, or related field

Job description

The Data Scientist plays a key role in planning, executing, and delivering machine learning-driven solutions that create measurable business impact.

This role involves analyzing complex datasets, developing AI/ML and optimization models, and translating insights into actionable recommendations.

The Data Scientist collaborates with business and technical teams to drive data-informed decision-making and supports the development of advanced analytics capabilities.

Key Responsibilities

• Collect, clean, and analyze large datasets from diverse sources, ensuring data quality and consistency.
• Develop and maintain data pipelines for efficient and repeatable data science workflows.
• Apply statistical techniques and exploratory data analysis methods such as clustering and PCA.
• Design, develop, and validate machine learning and optimization models for classification, regression, clustering, and prediction tasks.
• Perform feature engineering, model selection, and evaluation to improve model performance and interpretability.
• Conduct experiments including A/B and multivariate testing to measure impact and validate hypotheses.
• Integrate domain knowledge into analytical solutions to enhance business outcomes.
• Collaborate with data engineers, MLOps, and IT teams to deploy and maintain machine learning models.
• Monitor and optimize production models to ensure performance and reliability over time.
• Create dashboards and visualizations to communicate insights effectively to stakeholders.
• Present complex findings to both technical and non-technical audiences using clear storytelling.
• Stay updated with emerging trends in AI/ML and recommend new tools and methodologies.
• Mentor junior team members and promote best practices in data science.

Required Qualifications

• Master’s or PhD in Computer Science, Data Science, Engineering, Statistics, Applied Mathematics, Operations Research, or a related quantitative field.
• 3–5 years of hands-on experience delivering end-to-end data science projects.
• Strong programming skills in Python or R.
• Experience with machine learning frameworks such as scikit-learn, TensorFlow, or PyTorch.
• Expertise in statistical analysis, machine learning techniques, and experimental design.
• Strong data engineering skills including SQL/NoSQL, data pipelines, and distributed computing tools such as Hadoop, Spark, or Kafka.
• Experience with cloud platforms such as AWS, Azure, or Google Cloud.
• Experience with MLOps practices including model deployment, monitoring, and containerization.
• Strong data visualization and communication skills using tools such as Tableau or Power BI.
• Ability to collaborate across teams and communicate effectively with diverse stakeholders.

Preferred Qualifications

• Healthcare domain experience, including familiarity with clinical workflows and healthcare data systems.
• Experience with Epic EHR systems.
• Relevant certifications such as Clarity/Caboodle, Google Cloud ML Engineer, or AWS Machine Learning Specialty.

Certifications

• Clarity/Caboodle, Google Cloud ML Engineer, or AWS Machine Learning Specialty (if applicable).