Senior Data Science Lead - R01566414

Experience Range: 12+ years of experience in data science, including hands-on expertise in advanced statistical modeling and machine learning

Key Responsibilities:
  1. Lead the design and implementation of complex data science solutions to drive business impact and inform strategic decision-making
  2. Develop, validate, and optimize advanced statistical and machine learning models, including regression, classification, and forecasting algorithms
  3. Collaborate with cross-functional teams to translate business objectives into actionable analytics projects and deliver measurable outcomes
  4. Mentor and guide junior data scientists, fostering a culture of technical excellence and continuous learning
  5. Leverage Python, R, and relevant frameworks to build scalable data pipelines and automate model deployment using tools such as KubeFlow and BentoML
  6. Conduct rigorous statistical analysis, including hypothesis testing, T-Test, Z-Test, and probabilistic graph modeling to uncover actionable insights
  7. Implement and monitor model validation, explainability, and performance tracking using tools like Great Expectation and Evidently AI
  8. Stay current with emerging trends in machine learning, artificial intelligence, and big data technologies to drive innovation within the team

Required Skills:
  1. Expertise in hypothesis testing, T-Test, and Z-Test
  2. Advanced proficiency in regression techniques (linear and logistic)
  3. Strong programming skills in Python and PySpark
  4. Experience with SAS or SPSS for statistical analysis and computing
  5. Hands-on knowledge of probabilistic graph models
  6. Proficiency with machine learning frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, or MXNet
  7. Forecasting techniques, including exponential smoothing, ARIMA, and ARIMAX
  8. Experience with model deployment tools such as KubeFlow and BentoML
  9. Strong understanding of classification algorithms (decision trees, SVM)
  10. Proficiency in R and R Studio

Preferred Skills:
  1. Experience with Great Expectation and Evidently AI for model validation and monitoring
  2. Knowledge of advanced distance metrics (Hamming, Euclidean, Manhattan)
  3. Expertise in scalable data engineering for machine learning pipelines
  4. Hands-on experience with cloud-based machine learning platforms
  5. Familiarity with MLOps best practices and CI/CD for data science

Desired Qualifications:
  1. Master’s or PhD in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field
  2. Relevant certifications in machine learning, data science, or analytics (e.g., TensorFlow, SAS, or equivalent)