Data Scientist

Sally Beauty Supply LLC; Plano (TX); On-site; USD 100,000 - 120,000; Full time

Benefits offered by this job

Medical

Dental

Vision

Life Insurance

Paid vacation and sick days

Paid holidays

Tuition reimbursement

401(k) with company match

In-house salon services

Job summary

Sally Beauty Supply LLC in Plano, TX is seeking a Data Scientist to turn customer data into ML models that improve Customer Lifetime Value (CLV). You will design and operate analytics and ML solutions in Databricks, supporting hyper-personalized CRM, retention, and proactive churn reduction.

You’ll translate model outputs into actionable recommendations for non-technical senior leadership, delivering production-ready components and scalable pipelines.

Qualifications

• Master’s degree in Mathematics/Statistics/Data Science or related field
• 4+ years hands-on data science or customer analytics
• Advanced proficiency in Python and SQL
• Experience with Databricks, Spark/PySpark, Delta Lake and cloud environments (Azure preferred; AWS/GCP acceptable)
• Knowledge of regression, classification, clustering (K-Means), survival analysis, recommender systems, and dimensionality reduction (PCA)
• Experience deploying and monitoring models (MLflow, drift detection, CI/CD)
• Ability to communicate insights to non-technical leadership

Responsibilities

• Develop ML models across full lifecycle using Python
• Drive churn, retention, CLV, and propensity-to-buy analytics
• Create customer segments using behavioral and value-based features
• Apply advanced analytics like Market Basket, survival analysis, uplift modeling
• Produce summary statistics, distributions, correlations, and validation checks
• Deliver ad hoc analyses to support planning and forecasting
• Build and maintain a customer 360 view and data pipelines in Databricks
• Develop production-ready components and ensure scalability
• Automate scoring and model retraining with monitoring for data/model drift
• Participate in analytics engineering practices (Git, Azure DevOps, CI/CD)
• Design and analyze A/B and multivariate tests for campaigns
• Maintain SOPs for measurement, data quality, and journey support

Skills

Python

SQL

Communication

Storytelling

Education

Master’s degree in Math/Statistics/Data Science

Tools

Databricks

Spark/PySpark

Delta Lake

Azure

AWS

GCP

MLflow

Git

Azure DevOps

REST APIs

Job description

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.