Senior Data Scientist - R01566194

Experience Range: 5 - 8 years of experience in advanced data science roles

Key Responsibilities:
  1. Design and implement advanced statistical models and machine learning algorithms to solve complex business challenges
  2. Develop, validate, and deploy predictive models using Python, R, or PySpark, ensuring high accuracy and scalability
  3. Conduct rigorous hypothesis testing, including T-Test and Z-Test, to drive data-driven decision making and uncover actionable insights
  4. Apply regression techniques such as linear and logistic regression to analyze trends, forecast outcomes, and optimize business processes
  5. Utilize time series forecasting methods, including ARIMA, ARIMAX, and exponential smoothing, to deliver accurate demand and trend predictions
  6. Leverage classification methods such as decision trees and support vector machines to segment data and enhance model performance
  7. Collaborate with cross-functional teams to translate business requirements into analytical solutions and communicate findings effectively
  8. Ensure model reliability and compliance by implementing robust validation frameworks and tools, including Great Expectations and Evidently AI

Required Skills:
  1. Advanced proficiency in Python and PySpark for data analysis and modeling
  2. Expertise in statistical analysis and computing, including hypothesis testing, T-Test, and Z-Test
  3. Hands-on experience with regression techniques (linear and logistic regression)
  4. Practical knowledge of probabilistic graph models
  5. Experience with forecasting methods such as exponential smoothing, ARIMA, and ARIMAX
  6. Proficiency in classification algorithms, including decision trees and support vector machines (SVM)
  7. Strong command of ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet
  8. Skilled in using statistical tools like SAS and SPSS
  9. Familiarity with R and R Studio for statistical modeling
  10. Experience with distance metrics (Hamming, Euclidean, Manhattan Distance)

Preferred Skills:
  1. Experience implementing data validation frameworks such as Great Expectations and Evidently AI
  2. Knowledge of model deployment tools like KubeFlow and BentoML
  3. Background in handling large-scale data processing and distributed computing environments
  4. Expertise in feature engineering and dimensionality reduction techniques
  5. Familiarity with automated machine learning (AutoML) pipelines

Desired Qualifications:
  1. Bachelor’s or Master’s degree in Computer Science, Statistics, Mathematics, Data Science, or a related quantitative field
  2. Relevant certifications in data science, machine learning, or statistical analysis are a plus