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 100,000 - 120,000 per year, along with a benefits package designed to support day-to-day life and long-term security.

What you’ll do

• 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 DevOps, 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.

What you’ll bring

• 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, NumPy, scikit-learn) and SQL.
• Experience with Databricks, Spark/PySpark, 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.

Tools you’ll use

Python, pandas, NumPy, scikit-learn, SQL, R, Databricks, Spark, PySpark, Delta Lake, Azure, AWS, GCP, MLflow, Git, Azure DevOps, REST APIs

Benefits

• Competitive salary and outstanding benefits package
• Medical, Dental, and Vision
• Life Insurance
• Paid vacation and sick days and paid holidays
• Tuition reimbursement
• 401(k) with company match
• In-house salon with complementary services
• Varied selection of food options at the corporate center

Working conditions

• Hybrid role requiring onsite presence at the corporate office on specified days.
• Work environment generally involves everyday risks or discomforts typical of offices, meeting and training rooms, retail stores, and residences or commercial vehicles, with normal safety precautions.
• Sedentary work; may include some walking, standing, bending, and occasional need to carry, move, and set up small hardware such as desktops, monitors, printers, and testing lab equipment.
• Work area is adequately lit, heated, and ventilated.