Data Scientist

Join Sally Beauty Supply LLC as a Data Scientist in Plano, TX (onsite) and help turn customer data into machine learning models that improve Customer Lifetime Value (CLV). You will build and operate end-to-end analytics and ML solutions in Databricks
, supporting hyper-personalized CRM, retention, and proactive churn reduction. You will also translate model outputs into clear, actionable recommendations for non-technical senior leadership
.

This role includes a competitive salary of USD 100, per year
, along with a benefits package designed to support day-to-day life and long-term security.

• Develop machine learning models across the full lifecycle including design, feature engineering, training, evaluation, validation, and implementation using Python
  .
• Drive high-impact customer analytics use cases such as churn and retention modeling
  , propensity-to-buy
  , CLV prediction
  , customer persona/segmentation
  , and next-best-action recommendations
  .
• Build segments beyond demographics using behavioral, psychographic, and value-based approaches (example methods include RFM
  , K-Means clustering
  , and propensity tiers
  ) so CRM and Marketing can activate them directly.
• Apply advanced analytics methods including Market Basket Analysis
  , survival analysis
  , uplift/incrementality modeling
  , and recommender approaches to identify cross-sell, up-sell, and hidden revenue opportunities.
• Produce disciplined analysis with summary statistics
  , distribution and correlation studies
  , and appropriate feature selection using confidence intervals and validation techniques such as cross-validation and model performance checks.
• Deliver priority ad hoc analysis that supports the SALLY plan and forecast
  , balancing speed with statistical accuracy.
• Own customer data foundations by building and maintaining a customer 360 view and data pipelines in Databricks using Python, PySpark, and SQL
  .
• Convert proof-of-concept work into production-ready, reusable components integrated into products and services, with attention to scalability (compute, memory, I/O, model serialization, caching).
• Automate ML operations such as scheduled scoring and model re-training
  , and implement monitoring for data drift
  , model drift
  , and accuracy degradation
  , including back-testing, explainability, reproducibility, and data quality checks.
• Support analytics engineering practices including source control
  , peer code review
  , and automated testing using Git and Azure Dev Ops
  , contributing to CI/CD for analytics assets.
• Design and analyze A/B and multivariate tests for email, SMS, push, and in-app campaigns to optimize engagement, conversion, and incremental lift, including statistically sound test and control audiences.
• Execute measurement frameworks for test vs. control and apply guardrails for attribution
  , incrementality
  , and performance readouts.
• Maintain SOPs for campaign measurement, reporting hygiene, and data integrity, and support customer journeys across Onboarding, Growth, Retention, and Reactivation
  .

• Master’s degree in mathematics / Statistics / Data Science and Analytics, Computer Science, Economics, Physics, or a related field (required). Master’s degree preferred.
• 4+ years of hands-on experience in data science, applied machine learning, or customer analytics.
• Advanced proficiency in Python (pandas, Num Py, scikit-learn) and SQL
  .
• Experience with Databricks
  , Spark/Py Spark ,
  Delta Lake
  , and a major cloud environment (Azure preferred; AWS/GCP acceptable).
• Working knowledge of regression, classification, clustering (K-Means),
  tree-based and boosting methods
  , survival analysis
  , recommender systems
  , and dimensionality reduction (PCA).
• Exposure to hypothesis testing
  , confidence intervals
  , experimental design
  , cross-validation
  , and basic probability and linear algebra.
• Experience with model deployment and monitoring (for example,
  MLflow
  ), model re-training automation, drift detection,
  Git
  , and code review practices.
• Solid PowerPoint and Excel skills to communicate executive-ready narratives.
• Helpful extras: R experience, exposure to deep learning frameworks, and familiarity with REST APIs, containerization, or orchestration tooling.

Python,…