Data Scientist II

Job Summary for Data Scientist II (List Format):

- Develop, test, and optimize machine learning models to address business challenges and provide actionable insights.
- Perform statistical analyses, forecasting, hypothesis testing, and predictive modeling on large, complex datasets.
- Collaborate with business stakeholders to identify opportunities for data science to improve decision-making and operational efficiency.
- Conduct exploratory data analysis to uncover patterns, trends, and business opportunities.
- Design and evaluate experiments to support strategic initiatives.
- Build and maintain analytical datasets, reports, dashboards, and data visualizations.
- Present findings and recommendations to both technical and non-technical audiences.
- Support the deployment, monitoring, and performance measurement of models in production environments.
- Work closely with data engineers, analysts, and technology teams throughout the data science lifecycle.
- Stay updated on emerging machine learning and AI techniques, recommending relevant applications.

Required Qualifications:
- Bachelor's degree in Data Science, Statistics, Computer Science, Mathematics, Engineering, or related quantitative field.
- 3-4 years of experience in data science, machine learning, or predictive analytics roles.
- Hands-on experience building and validating machine learning models using Python.
- Strong knowledge of supervised and unsupervised learning techniques.
- Proficient in statistical analysis, predictive modeling, and data mining methodologies.
- Advanced SQL skills; experience working with large datasets.
- Experience with visualization and reporting tools (e.g., Power BI).
- Excellent communication skills, with the ability to translate technical findings into business recommendations.

Preferred Qualifications:
- Master's degree in a quantitative discipline.
- Experience with AWS, Azure, or other cloud-based environments.
- Familiarity with MLOps, including model deployment, monitoring, CI/CD, and lifecycle management.
- Experience with machine learning frameworks (e.g., Scikit-Learn, TensorFlow, PyTorch, XGBoost).
- Knowledge of predictive analytics, forecasting, optimization, customer analytics, or operational analytics use cases.