Data Science Lead (Classic ML)
10+ years required
Remote role,
W2(Independent Contractors)
Experience Range: 10+ years of experience in advanced data science roles, including leadership of machine learning and statistical modeling projects
Key Responsibilities:
1. Lead the design, development, and deployment of Next Best Offer models and advanced data science solutions to drive business growth
2. Apply statistical techniques including hypothesis testing, t-tests, z-tests, regression (linear and logistic), and forecasting to generate actionable insights
3. Oversee end-to-end machine learning workflows using Python, PySpark, and R, ensuring robust model development, validation, and continuous improvement
4. Leverage probabilistic graph models and advanced classification algorithms such as decision trees and support vector machines to address complex business challenges
5. Implement and optimize scalable machine learning pipelines using KubeFlow and BentoML for production environments
6. Conduct statistical analysis and computing utilizing SAS, SPSS, and R Studio to support data-driven decision-making
7. Evaluate, monitor, and refine model performance to ensure accuracy, reliability, and business impact
8. Collaborate with cross-functional teams to translate business requirements into effective data-driven strategies and measurable outcomes
Required Skills:
1. Python
2. PySpark
3. SAS
4. SPSS
5. R
6. R Studio
7. Probabilistic graph models
8. Regression methods (linear and logistic)
9. Forecasting methods (exponential smoothing, ARIMA, ARIMAX)
10. Decision trees
11. Support Vector Machines (SVM)
12. TensorFlow
13. PyTorch
14. Scikit-learn
15. CNTK
16. Keras
17. MXNet
18. KubeFlow
19. BentoML
Preferred Skills:
1. Great Expectation
2. Evidently AI
3. Cloud-based machine learning deployment
4. Advanced ensemble methods
5. Boosting algorithms
6. A/B testing
7. Experimental design
8. Recommendation systems
9. Personalization algorithms
Desired Qualifications:
1. Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
2. Certification in Machine Learning or Data Science from a recognized institution such as Coursera, edX, or DataCamp
3. Relevant certification in statistical analysis or analytics, such as SAS Certified Statistical Business Analyst
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