Sr. Data Science Analyst

Sally Beauty•Plano, TX

18h•$100,000 - $120,000•Hybrid

About The Position

The Senior Data Science Analyst is a hands-on technical contributor who builds predictive models, advanced analytics solutions, and production-ready data products to help Sally Beauty Holdings understand and activate customer behavior. The role combines data science, machine learning, statistical analysis, and data engineering to support customer acquisition, retention, engagement, and lifetime value initiatives. Working across advanced analytics and commercial strategy, the analyst partners with Customer Analytics, CRM, Marketing, Merchandising, and Data Engineering teams to develop scalable models and insights that enable personalized customer experiences. The role owns the end-to-end lifecycle of data science initiatives, from problem definition and feature engineering through model development, validation, deployment, monitoring, and ongoing optimization. This is a hybrid role based at the Legacy West Support Center in Plano, Texas, with onsite work required on specified days.

Requirements

At least 4 years of experience in data science, machine learning, customer analytics, or advanced analytics roles.
Advanced proficiency in Python, including pandas, NumPy, scikit-learn, and related data science libraries.
Advanced proficiency in SQL for data extraction, transformation, and analysis.
Experience with Databricks, Spark/PySpark, Delta Lake, and cloud-based analytics environments.
Strong understanding of machine learning techniques, including regression, classification, clustering, tree-based models, boosting algorithms, survival analysis, recommender systems, and dimensionality reduction.
Solid foundation in statistics, experimental design, hypothesis testing, confidence intervals, cross-validation, probability, and predictive modeling.
Experience deploying, monitoring, and maintaining production machine learning solutions using MLflow or similar frameworks.
Knowledge of software development best practices, including Git, code reviews, testing methodologies, and CI/CD processes.
Strong proficiency in Microsoft Excel and PowerPoint, with the ability to communicate analytical findings through executive-ready presentations.
Ability to manage multiple priorities and deliver high-quality analytical solutions in a fast-paced environment.
Exceptional analytical thinking, problem-solving, and business acumen.

Nice To Haves

Master’s degree in Data Science, Statistics, Mathematics, Computer Science, Economics, Physics, Analytics, or a related quantitative field.
Experience in retail, ecommerce, consumer packaged goods (CPG), loyalty, subscription, or other customer-focused industries.
Experience with cloud-based analytics environments, preferably Microsoft Azure.

Responsibilities

Develop, validate, deploy, and monitor machine learning models for customer acquisition, retention, churn reduction, customer lifetime value, segmentation, propensity scoring, and next-best-action recommendations.
Design and implement customer segmentation strategies using behavioral, transactional, demographic, and value-based data.
Apply clustering, regression, classification, survival analysis, recommender systems, and market basket analysis to identify business opportunities.
Conduct exploratory data analysis, feature engineering, model evaluation, and validation to ensure high-quality, statistically sound outcomes.
Develop customer-focused analytics and predictive assets for CRM campaigns, loyalty initiatives, and customer lifecycle management.
Build and maintain scalable customer data pipelines and customer 360 solutions in Databricks using Python, PySpark, and SQL.
Turn analytical prototypes into production-ready solutions focused on scalability, reliability, maintainability, and business impact.
Implement and maintain MLOps processes, including automated model scoring, retraining, performance monitoring, drift detection, and data quality validation.
Follow software engineering best practices, including version control, peer code review, testing, and CI/CD deployment.
Design, execute, and analyze A/B tests and multivariate experiments across email, SMS, push notifications, and mobile applications.
Develop statistically valid test and control audiences to measure campaign effectiveness and incremental business impact.
Partner with CRM and Marketing teams to optimize onboarding, growth, retention, and reactivation customer journeys.
Translate analytical findings into clear business recommendations for technical and non-technical stakeholders.
Present insights, forecasts, and recommendations to senior leadership and cross-functional partners.
Support ad hoc analyses, strategic initiatives, and special projects that advance customer and business objectives.