Senior Data Science Analyst

Senior Data Science Analyst | Corporate 

This position is Hybrid working from our Legacy West Support Center located in Plano, Texas.

About Sally Beauty Holdings, Inc.

At SBH, our purpose is to inspire a more colorful, confident, and welcoming world. We are the leader in professional hair color, selling and distributing professional beauty supplies across 11 countries through our Sally Beauty and Beauty Systems Group businesses. Sally Beauty offers products for hair color, hair care, nails, and skin care to retail customers looking for salon quality products at a value price. Beauty Systems Group, branded as Cosmo Prof or Armstrong McCall stores, along with its direct sales consultants, sell professionally branded products intended for use and resale by salons to retail consumers.

About the Role

The Sr Data Science Analyst  builds the models and data products that power how SBH understands and activates its customers. This is a technical individual-contributor role for a Sr Data Science Analyst who is equally comfortable engineering a feature pipeline in Databricks, defending a model's validation approach, and translating results into clear business recommendations for non-technical leaders.

Your mission is to increase Customer Lifetime Value (CLV) by independently developing the predictive assets behind hyper-personalized CRM campaigns, retention strategies, and proactive churn reduction. You will own models end to end, from problem framing and feature engineering through training, validation, deployment, and ongoing performance monitoring, and ensure they run reliably in production rather than living in a notebook.

This role sits at the intersection of advanced analytics and commercial strategy and is critical to advancing SBH's Understand & Activate Customer agenda. You will work meticulously  with Data Engineering, CRM, Marketing, and Merchandising partners, and will help mature our Databricks environment from a place where analysis happens into a platform that delivers repeatable, monitored, production-grade customer intelligence. As a senior member of the team, you will set the technical direction for the models you own, raise the bar on modeling and code quality through review and mentorship, and act as a trusted analytics advisor to Director- and VP-level partners across the Sally Beauty and BSG/CosmoProf banners.

Responsibilities 

Model Development & Advanced Analytics (50%)

• Predictive Modeling:  Build machine learning models through all phases of development, design, feature engineering, training, evaluation, validation, and implementation, using Python. Core use cases include churn and retention modeling, propensity-to-buy, CLV prediction, customer persona/segmentation, and next-best-action recommendations.

• Segmentation Strategy:  Move beyond basic demographics to build behavioral, psychographic, and value-based segments (e.g., RFM, K-Means clustering, propensity tiers) that CRM and Marketing can activate directly.

• Advanced Methodologies:  Apply Market Basket Analysis (MBA), survival analysis, uplift/incrementality modeling, and recommender approaches to uncover cross-sell, up-sell, and hidden revenue opportunities.

• Statistical Rigor:  Produce summary statistics, distribution and correlation studies, and disciplined feature selection; apply confidence intervals, cross-validation, and appropriate model performance validation to every deliverable.

• Ad Hoc Agility:  Prioritize and deliver critical ad hoc analysis supporting the SALLY plan and forecast, balancing speed with statistical accuracy.

• Problem Framing & Solution Ownership:  Take ambiguous, loosely defined business questions and independently translate them into an analytical roadmap: choose the right methodology, define success metrics and business KPIs up front, set scope and timelines, and make the trade-off calls between speed, accuracy, and complexity without close supervision.

• Business Impact Accountability:  Quantify and own the incremental business value of every model and analysis delivered (e.g., revenue, margin, retention, and CLV lift), track realized versus expected impact after launch, and present results and recommendations directly to Director- and VP-level stakeholders.

• Model Review & Governance:  Act as a technical reviewer for models built by peers and junior analysts, challenging validation design, target leakage, sampling bias, explainability (e.g., SHAP), and production-readiness before models are approved for deployment.

• Develop meaningful and lasting relationships with partners for optimized systems integration, and respond to questions and concerns from managers and executives with supporting research and recommendations

Data Engineering for Analytics & Productionization (30%)

• Data Pipelines:  Develop and maintain the customer 360 view and supporting data pipelines in Databricks using Python, PySpark, and SQL. You should be comfortable building your own pipelines to enable your analysis rather than waiting on others.

• Production-Ready Code:  Convert proof-of-concept code into production-ready, re-usable components integrated into products and services, with attention to scalability (compute, memory, I/O, model serialization, caching).

• MLOps:  Automate ML processes such as scheduled scoring and model re-training, and implement monitoring for data drift, model drift, and accuracy degradation. Contribute to impact assessment, back-testing, explainability, reproducibility, and data quality checks.

• Engineering Discipline:  Follow source control, peer code review, and automated testing practices (Git/Azure DevOps); contribute to CI/CD for analytics assets.

• Standards & Reusable Frameworks:  Define and champion team-wide standards for feature engineering, shared feature tables, pipeline templates, and model documentation in Databricks and Unity Catalog, so that new models can be built faster and maintained consistently by the broader team.

• Production Ownership:  Serve as the accountable owner for the production health of the models and pipelines you build, including scoring SLAs, failure triage, root-cause analysis, and clear hand-off documentation for onshore and offshore team members.

Experimentation, Test & Learn (20%)

• Test Design:  Design and analyze A/B and multivariate tests for email, SMS, push, and in-app campaigns to optimize engagement, conversion, and incremental lift.

• Audience Construction:  Build statistically sound test and control audiences in partnership with CRM, ensuring balance, mutual exclusivity, and reproducibility.

• Rigorous Measurement:  Execute the Test vs. Control measurement framework and apply guardrails for attribution, incrementality, and performance readouts.

• Standardization:  Apply and help maintain Standard Operating Procedures (SOPs) for customer campaign measurement, reporting hygiene, and data integrity.

• Lifecycle Optimization:  Support data-driven customer journeys (Onboarding, Growth, Retention, Reactivation) with the models and audiences that make them work.

• Measurement Strategy & Advanced Causal Methods:  Shape the test-and-learn roadmap with CRM and Marketing, and go beyond standard A/B testing when randomization is not practical, applying power and sample-size analysis, holdout design, variance reduction (e.g., CUPED), difference-in-differences, propensity matching, or synthetic control to measure true incrementality.

• SOP Ownership:  Author and evolve, not just follow, the Standard Operating Procedures for campaign measurement and test governance, and act as the final quality check on test readouts before they reach leadership.

Knowledge, Skills & Abilities Requirements

Education & Experience

• Education:  Master's degree is required in Mathematics/Statistics/Data Science and Analytics, Computer Science, Economics, Physics, or a related field. 

• Experience: 6+ years of hands-on experience in data science, applied machine learning, or customer analytics.

• Leadership Experience:  Demonstrated track record of independently leading end-to-end data science projects from problem definition to production deployment with measurable business impact, and of mentoring or technically guiding other analysts.

• Industry:  Experience in Retail, CPG, e-commerce, or subscription/loyalty-driven businesses is highly desirable.

Technical Skills

• Core Languages:  Advanced proficiency in Python (pandas, NumPy, scikit-learn) and SQL. Experience with R is a plus.

• Big Data & Cloud:  Hands-on experience with Databricks, Spark/PySpark, Delta Lake, and a major cloud environment (Azure preferred; AWS/GCP acceptable).

• Machine Learning:  Regression, classification, clustering (K-Means), tree-based and boosting methods, survival analysis, recommender systems, and dimensionality reduction (PCA). Exposure to deep learning frameworks is a plus.

• Statistical Knowledge:  Solid grounding in hypothesis testing, confidence intervals, experimental design, cross-validation, and basic probability and linear algebra.

• MLOps & Engineering:  Experience with model deployment and monitoring (e.g., MLflow), model re-training automation, drift detection, version control (Git), and code review practices. Exposure to REST APIs, containerization, or orchestration tooling is a plus.

• Presentation:  Strong PowerPoint and Excel skills, with the ability to build an executive-ready narrative.

• Causal Inference & Advanced Methods:  Strong working knowledge of causal inference and incrementality measurement (uplift modeling, difference-in-differences, propensity score methods), plus exposure to Bayesian methods, time-series forecasting, or optimization techniques.

• Stakeholder Influence:  Proven ability to influence decisions and priorities with senior business leaders, push back with data when needed, and turn complex model outputs into clear, actionable strategy.

• Emerging Technology:  Exposure to Generative AI / LLM applications (e.g., customer insight summarization, content personalization, or analytics copilots) on Databricks or Azure is a plus.

Competencies & Attributes

Ability to “connect the dots” across disparate data points to form a cohesive business recommendation. A self-starter who operates independently, seeks opportunities to scale and automate, is comfortable challenging the status quo, and can present findings credibly to non-technical senior leadership. Outstanding organizational skills and dedication to quality and integrity, with the ability to contribute effectively in a fast-paced environment.

• Passionate Learner – inquisitive about the business; open to feedback and coaching, applies learning quickly; applies learning to improve processes and procedures, proactively shares learning with colleagues and leaders; realigning and reshaping projects

• Flexible & Agile Adapter – responsive and open to change; works well with ambiguity; adapts to new plans or directions; keeps calm under pressure; perseveres to achieve the plan/task; doesn't dwell on the past

• Effective Communicator – articulates in an appropriate and accurate manner; emotionally astute while remaining authentic to own style/self; encourages others to express views and opinions; demonstrates active listening and uses probing questions; is concise and relevant with data/info

• Team Builder – references the importance of teamwork and actively demonstrates collaboration and sharing; builds and/or participates in effective teams; values the importance of inclusion and various sources of thought/input; humble when operating within a team

• Customer Focused Partner – understands internal and external customer needs; contributes to plans and actions to improve the associate and customer journey/experience; holds self and team accountable for improving the customer experience; is an advocate for the customer

• Strategic Thinker – progressive thinking with the ability to bring new ideas to life; works with others to develop progressive and cost-effective strategies; provides suggestions to improve upon continuous improvement and scalability within department; uses a broad range of data sources

• Big Picture Thinker – understands own department and how other key departments operate; adopts an inclusive approach; seeks feedback, reviews progress, and adapts plans as needed; understands interdependencies with other departments

• Results Driver – effective at driving and delivering on plans; holds self-accountable to a high standard of delivery; suggests opportunities for innovation and continuous improvement; focuses on the right priorities and uses resources/time wisely; demonstrates grit and determination

• Problem Solver & Decision Maker – able to consume department/operational data to identify business issues; identifies, gathers, and examines the relevant information; makes recommendations and takes action to solve challenges; considers importance/impact of decisions against relevant factors

• Mentor & Technical Coach –  proactively develops junior analysts through code and model reviews, pairing, and knowledge-sharing sessions; makes the people around them better and helps scale the team's capabilities

• Trusted Advisor & Influencer –  builds credibility with senior leaders by bringing a point of view, not just numbers; challenges assumptions constructively; shapes business decisions and analytics priorities

Working Conditions & Physical Requirements

This will be a hybrid role required to be onsite at the corporate office on specified days. The work environment generally involves everyday risks or discomforts which require normal safety precautions typical of such places as offices, meeting and training rooms, retail stores, and residences or commercial vehicles, e.g., use of safe work practices with office equipment, avoidance of trips and falls, observance of fire regulations and traffic signals, etc. The work area is adequately lighted, heated, and ventilated.