Data Scientist II

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.

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.