Data Scientist

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.

• 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.

• 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, Tensor Flow, or PyTorch.
• Expertise in statistical analysis, machine learning techniques, and experimental design.
• Strong data engineering skills including SQL/No
  
  SQL, 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.

• 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.

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