DATA SCIENTIST - REVENUE AND MARKETING

Storage Asset Management is a property management and consulting company that specializes in self-storage. With over 70 years of executive industry experience, SAM employs an incredible team of employees on every level. Voted Best Places to work in PA 2021,2022,2023, 2024, and 2025!

Storage Asset Management is proud to have over 900 employees at the store, regional and corporate office level.

Summary: This position will work with large and complex data sets to develop models, generate insights, and drive data-informed decisions across revenue and marketing departments specifically. This position will also collaborate with cross-functional teams to understand business needs and translate them into analytical solutions across the organization. This role reports to the Chief Growth Officer on a solid-line basis and holds a dotted-line, contributing relationship to the Data team, so the work stays connected to broader data infrastructure, standards, and tooling decisions.

Benefits of Joining SAM:

• A dynamic company with an award-winning culture

• A clear path for advancement within our thriving company

• Competitive pay with bonus potential

• Paid holidays

• Paid time Off

• Paid maternity and paternity leave

• Comprehensive healthcare and 401(k) plan

• Short-Term & Long-Term Disability insurance

• Flexible Scheduling

•Tuition re-imbursement

• Dental Insurance

• Health Insurance

• Vision Insurance

Essential Duties & Responsibilities:

• Collect, clean, and analyze structured and unstructured data from various sources.

• Develop predictive models, machine learning algorithms, and statistical analyses to solve revenue management marketing problems.

• Assist in developing dynamic pricing models based upon market conditions such as demand, saturation, and occupancy.

• Design and conduct experiments to test hypotheses and measure impact.

• Visualize data and present findings in a clear and actionable manner to stakeholders.

• Build, validate, and deploy machine learning models for a variety of use cases such as customer segmentation, recommendation systems, anomaly detection, churn prediction, demand forecasting, and more.

• Develop and automate robust data pipelines for training and scoring models using tools.

• Design and run A/B tests and experiments to evaluate model performance and business impact.

• Design and pilot test AI-driven models for advancing revenue management and marketing.

•Incorporate marketing performance data (lead volume, source-level conversion, lead achievement vs. revenue achievement gaps) into pricing and evidence gate models across all sites, so rate decisions account for demand-side signal, not just occupancy and unit-type data.

• Flag sites where marketing-driven demand shifts could produce false signals in the pricing model, so evidence gate thresholds aren't triggered by a marketing event instead of a genuine market shift. At full-portfolio scale this needs to run unattended across hundreds of sites, not get manually reviewed site-by-site.

• Proactively identify pricing or model opportunities beyond current pilot scope, flag emerging risks (regulatory, model drift, data quality) before they surface in production, bring point-of-view recommendations rather than waiting for direction.

• Communicate results and recommendations clearly through reports, dashboards, and presentations tailored to both technical and non-technical audiences.

• Stay current with the latest advancements in machine learning, AI, and data science through research, experimentation, and collaboration.

• Support the long-term strategic planning process, understanding the broader business landscape (competitors, clients, pricing trends).

• Identify opportunities to create automation for frequently used processes, reports and systems and lead team to complete those projects.

• Collaborates with appropriate coworkers to gather input, feedback, and needs.

• Perform other ad-hoc reporting and analysis for the organization.

• In office scheduling requirements are determined by your supervisor and are subject to change at any time

Education and/or Experience Requirements:

• 6+ years in pricing, revenue management, data science, or directly adjacent quantitative role. Must include hands-on model-building, not analysis of vendor-provided models

• 1+ years of applied AI experience in a production or applied context: LLM-based tooling, embeddings, RAG, classification, or agentic workflows. Needs to be a specific, demonstrable project, not a training or self-study claim

• Bachelor’s Degree or similar experience in related position; Master’s degree preferred, or an equivalent combination of experience and/or education

• Experience taking a model from pilot to production at scale (10x+ site or unit expansion), not just building a proof of concept. Ask for a specific example of what broke during that scale-up, not just that they did it

• Track record of identifying a pricing or modeling improvement that wasn't asked for and driving it to implementation

• Highly organized with an aptitude for problem-solving

• Demonstrated experience building and deploying end-to-end machine learning models (classification, regression, clustering, NLP, etc.).

• Proficiency in Python (e.g., pandas, scikit-learn, NumPy, PyTorch, TensorFlow) or R, and solid understanding of object-oriented programming and software engineering principles.

• Strong experience with SQL and relational databases; familiarity with Azure or Microsoft SQL is a plus.

• Deep understanding of statistical methods, hypothesis testing, and experimental design.

• Experience working with cloud platforms such as AWS, GCP, or Azure.

·• Attention to detail and a high level of accuracy and confidentiality

• Great time management

• Exceptional presentation and analytical skills

• Advanced knowledge of Excel

Language Skills: Must be fluent in speaking and writing the English language.

Work Hours: In a typical week, this position requires a minimum of 40 hours with regular and predictable attendance. May include occasional weekend work.

Work Environment: This work is usually performed indoors in an office environment with normal noise levels and no exposure to hazardous conditions. Employees in this position are required to reside in a state where SAM is registered to conduct business.

SAM is committed to creating a diverse environment and is proud to be an equal opportunity employer. All qualified applicants will receive consideration for employment without regard to race, color, ethnicity, national origin, religion, gender, gender identity or expression, sexual orientation, genetic information, disability, age, veteran status, and other protected statuses as required by applicable law.