What are the responsibilities and job description for the Sr. Data Scientist position at Cloudious LLC?
Cleint is seeking a highly analytical and detail-oriented Data Scientist with expertise in sensitivity analysis to join our growing data science team. In this role, you will design, develop, and apply advanced statistical and machine learning models to assess the sensitivity of key outputs to variations in input data and assumptions. Your work will help uncover the most influential drivers of business outcomes, inform strategic decisions, and improve model robustness across the organization.
Key Responsibilities:
Develop and implement sensitivity analysis frameworks for models and business processes.
Design experiments and simulations to evaluate the effect of input variability on model predictions or business metrics.
Apply statistical and machine learning methods (e.g., Monte Carlo simulations, Sobol indices, Shapley values) for variance decomposition and feature importance.
Collaborate with cross-functional teams (e.g., Product, Finance, Engineering) to translate business problems into data science solutions.
Communicate findings through compelling visualizations, dashboards, and written reports.
Support model validation and risk assessment through robust sensitivity testing.
Continuously improve methodologies to ensure accuracy, scalability, and efficiency.
Required Qualifications:
Bachelor's or Master's degree in Data Science, Statistics, Applied Mathematics, Computer Science, or a related field.
2 years of experience in data science or quantitative analysis.
Strong background in sensitivity analysis techniques and their applications.
Proficiency in Python, R, or similar programming languages.
Experience with tools such as NumPy, pandas, scikit-learn, and sensitivity analysis libraries (e.g., SALib).
Strong understanding of statistical modeling, uncertainty quantification, and feature importance.
Excellent problem-solving, communication, and collaboration skills.
Preferred Qualifications:
PhD in a quantitative field.
Experience with model risk assessment or model validation in regulated environments (e.g., finance, healthcare).
Familiarity with Bayesian methods or global sensitivity analysis techniques.
Knowledge of cloud platforms (AWS, GCP, or Azure) and big data technologies.
