Data Scientist
Role details
Job location
Tech stack
Job description
Advanced Analytical Modeling (45%) Formulate and deploy complex analytical solutions, including causal and time-series forecasting, econometric modeling, and mathematical optimization. Conduct rigorous error analysis to iterate on and improve model performance for large-scale predictions. Data Engineering & Feature Discovery (20%) Assist in the collection, cleaning, and organization of large structured and unstructured datasets. Identify causes of variability, test new features, and integrate them into production-level code. Stakeholder Communication & BI (15%) Utilize business intelligence tools to create compelling visualizations that translate complex findings into actionable insights for both technical and non-technical executive leadership. Research & Innovation (10%) Explore and pilot state-of-the-art analytical tools and emerging technologies that can be leveraged to improve team efficiency and model accuracy. Technical Leadership & Mentorship (10%) Lead and manage a pipeline of talent, including interns, co-ops, and associate-level data scientists, providing coaching on model development methodologies and continuous improvement.
Requirements
Education: Master's Degree in Industrial Engineering, Operations Research, Mathematics, Statistics, Computer Science, or Data Science (Doctoral degree preferred). Technical Mastery: Machine Learning & Stats: Proficiency with data mining, statistical analysis, and machine learning platforms (e.g., AzureML). Programming: Expert-level skills in SQL, Python, and R. Automation & Apps: Experience with process automation (Power Automate) and low-code app development (Power Apps). Visualization: Expertise in Power BI or Tableau. Experience: 3+ years of applied experience in analytical model design, development, and deployment. 5+ years of experience in high-volume consumer environments (e.g., hospitality, retail, or entertainment) is preferred. Attributes: Superior conceptual thinking and creative problem-solving skills. Demonstrated leadership and mentorship ability in an Agile or continuous improvement environment. Willingness to support a 24/7/365 operation, including peak demand periods (weekends and holidays).