Senior Data Science Lead

Responsibilities

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

• 12+ years of experience in data science, with hands‑on expertise in advanced statistical modeling and machine learning.
• Location:
  
  Deerfield Beach, FL.
• Work type:
  Hybrid, minimum 3 days onsite per week.
• Expertise in hypothesis testing, T‑Test, and Z‑Test.
• Advanced proficiency in regression techniques (linear and logistic).
• Strong programming skills in Python and PySpark.
• Experience with SAS or SPSS for statistical analysis.
• Hands‑on knowledge of probabilistic graph models.
• Proficiency with machine learning frameworks such as Tensor Flow, PyTorch, Scikit‑Learn, CNTK, Keras, or MXNet.
• Forecasting techniques, including exponential smoothing, ARIMA, and ARIMAX.
• Experience with model deployment tools such as Kube Flow and BentoML.
• Strong understanding of classification algorithms (decision trees, SVM).
• Proficiency in R and RStudio.
• Prefer experience with Great Expectations and Evidently AI for model validation and monitoring.
• Knowledge of advanced distance metrics (Hamming, Euclidean, Manhattan).
• Expertise in scalable data engineering for machine learning pipelines.
• Hands‑on experience with cloud‑based machine learning platforms.
• Familiarity with MLOps best practices and CI/CD for data science.
• Master’s or PhD in Data Science, Statistics, Computer Science, Mathematics, or a related quantitative field.
• Relevant certifications in machine learning, data science, or analytics (e.g., Tensor Flow, SAS).