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About This Role
Description:
*The Role:*
The Senior Data Analyst / Data Scientist will lead business intelligence, analytics, reporting, and decision\-support for a growing self\-storage and real estate organization. Reporting to the Chief Financial Officer, this hands\-on role transforms financial, operational, customer, and market data into actionable insights that improve revenue, occupancy, budgeting, forecasting, customer service, and portfolio performance. The successful candidate will be:
- Highly organized, detail\-oriented, and able to connect analysis to the broader business strategy.
- Proactive, resourceful, and comfortable working independently in a fast\-paced environment.
- Customer\-service focused and responsive to the needs of executives, corporate teams, and field operations.
- A practical problem solver who communicates complex findings clearly to non\-technical stakeholders.
- Collaborative, adaptable, accountable, and willing to work on\-site in York, Pennsylvania.
*Essential Duties:*
Specific duties and functions of the position include, but are not limited to:
- Business Intelligence and Power BI:
o Serve as the company’s Power BI subject\-matter expert; design, develop, deploy, and maintain executive, financial, operational, revenue\-management, marketing, and property\-level dashboards.
o Build scalable data models using Power Query, DAX, semantic modeling, automated refreshes, permissions, documentation, and quality controls.
o Replace manual spreadsheet reporting with accurate, repeatable, user\-friendly reporting solutions and train employees to interpret and use the information.
- Self\-Storage, Revenue Management, and Customer Analytics:
o Analyze occupancy, economic occupancy, move\-ins, move\-outs, achieved and street rates, discounts, concessions, delinquency, retention, lead conversion, and unit\-type performance.
o Partner with operations and revenue management to evaluate pricing, rate increases, promotions, demand patterns, customer behavior, competitive conditions, and underperforming properties.
o Measure the impact of operational, marketing, pricing, and customer\-service initiatives and identify opportunities to improve revenue, occupancy, retention, and net operating income.
- Budgeting, Forecasting, and Financial Analysis:
o Support annual property and portfolio budgets, monthly forecasting, variance analysis, scenario planning, and performance comparisons to budget, prior year, forecast, and underwriting.
o Analyze revenue, operating expenses, NOI, capital expenditures, and other financial measures; clearly explain significant variances and business implications.
o Assist with acquisition underwriting, due diligence, post\-closing performance tracking, and integration reporting as requested.
- Data Science, Automation, and Systems Integration:
o Develop statistical, predictive, machine\-learning, or AI\-supported models when appropriate for revenue forecasting, pricing optimization, customer retention, lead conversion, and property performance.
o Develop, maintain, or support APIs and other integrations among operational, accounting, accounts\-payable, revenue\-management, and reporting platforms.
o Create reliable data pipelines and automated workflows that consolidate information, reduce duplicate entry, and eliminate unnecessary manual processes.
o Evaluate emerging analytical and AI technologies based on business value, scalability, cost, security, and user adoption.
- Data Governance, Quality, and Collaboration:
o Establish consistent KPI definitions, reconcile outputs to source systems and accounting records, troubleshoot discrepancies, and maintain documentation of data sources and methodologies.
o Protect confidential company, customer, employee, and financial information and support sound data\-governance practices.
o Translate business questions into practical analytical solutions, manage competing priorities, and provide responsive service to internal customers and third\-party technology partners.
Requirements:
*Experience and Qualifications:*
We’re looking for someone with the following:
- Bachelor’s degree in data analytics, data science, statistics, mathematics, computer science, finance, accounting, economics, business, or a related field.
- Five or more years of progressively responsible experience in business intelligence, analytics, financial analysis, data science, or a comparable role.
- Expert\-level Power BI experience, including dashboard design, DAX, Power Query, data modeling, visualization, administration, and automated reporting.
- Self\-storage experience strongly preferred, including property operations, budgeting, revenue management, pricing, occupancy, and multi\-location performance analysis.
- Advanced Excel skills; working knowledge of SQL, relational databases, and integration of data from multiple platforms.
- Experience with API development or integration. Python, R, Microsoft Fabric, Azure, data warehouses, machine learning, or AI tools are additional advantages.
- Experience with Sage 100, AvidXchange, Self Storage Manager or comparable property\-management software, and Veritec or comparable revenue\-management technology is a plus.
- Excellent analytical judgment, written and verbal communication, project management, organization, accuracy, and ability to perform under changing priorities.
- Ability and willingness to work on\-site in York, Pennsylvania.
*Core Competencies:*
Business and financial acumen; Power BI and data\-visualization expertise; self\-storage and revenue\-management knowledge; analytical problem solving; systems thinking; process improvement; data storytelling; customer\-service orientation; intellectual curiosity; initiative; accuracy; accountability; adaptability; and cross\-functional collaboration.
*Physical Requirements:*
- Duties are generally performed in a standard indoor office environment.
- Ability to remain stationary and use a computer for prolonged periods; occasional bending, reaching, and repetitive motion may be required.
Salary Context
This $125K-$150K range is below the median for Data Scientist roles in our dataset (median: $160K across 258 roles with salary data).
View full Data Scientist salary data →Role Details
About This Role
Data Scientists extract insights and build predictive models from data. In the AI era, many roles now include LLM-powered analytics, automated reporting, and integration with generative AI tools. The role has evolved from 'the person who runs SQL queries' to 'the person who builds AI-powered data products.'
Modern data science roles fall into two camps: analytics-focused (insights, dashboards, experimentation) and ML-focused (building predictive models, recommendation systems, NLP features). The best data scientists can operate in both modes. The AI shift means that even analytics-focused roles now involve building automated insight pipelines using LLMs, going well beyond one-off reports.
Across the 4,317 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Investment Real Estate, LLC, this role fits into their broader AI and engineering organization.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
What the Work Looks Like
A typical week includes: analyzing experiment results for a product feature launch, building a predictive model for customer churn, creating an automated reporting pipeline using LLM-powered summarization, presenting insights to stakeholders, and cleaning data (always cleaning data). The ratio of analysis to engineering varies by company, but expect both.
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
Skills Required
Python, SQL, and statistical modeling are the foundation. Increasingly, roles want experience with LLMs for data analysis, automated insight generation, and building AI-powered data products. Familiarity with cloud data platforms (Snowflake, BigQuery, Databricks) and ML frameworks (scikit-learn, PyTorch) covers most job requirements.
Experimentation design and causal inference are underrated skills that separate strong candidates. Companies care about whether their product changes cause improvements, and can distinguish causation from correlation. A/B testing methodology, Bayesian statistics, and the ability to communicate uncertainty to non-technical stakeholders are high-value skills.
Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
Compensation Benchmarks
Data Scientist roles pay a median of $192,890 based on 789 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400. This role's midpoint ($137K) sits 29% below the category median. Disclosed range: $125K to $150K.
Across all AI roles, the market median is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. For comparison, the highest-paying categories include AI Safety ($287,500) and Research Engineer ($272,100). By seniority level: Entry: $110,000; Mid: $194,400; Senior: $227,400; Director: $274,554; VP: $241,000.
Investment Real Estate, LLC AI Hiring
Investment Real Estate, LLC has 1 open AI role right now. They're hiring across Data Scientist. Based in York, PA, US. Compensation range: $150K - $150K.
Location Context
Across all AI roles, 15% (635 positions) offer remote work, while 3,657 require on-site attendance. Top AI hiring metros: New York (1,650 roles, $220,000 median); San Francisco (1,335 roles, $265,000 median); Los Angeles (708 roles, $214,112 median).
Career Path
Common paths into Data Scientist roles include Data Analyst, Statistician, Quantitative Researcher.
From here, career progression typically leads toward Senior Data Scientist, ML Engineer, AI Product Manager.
Start with statistics and SQL. Build a real analysis project on public data that demonstrates insight generation alongside model building. The market values data scientists who can communicate findings clearly to business stakeholders. If you want to move toward ML engineering, invest in software engineering fundamentals and production deployment skills.
What to Expect in Interviews
Interviews combine statistics, coding, and business acumen. SQL is almost always tested, often with complex joins and window functions. Expect a case study round where you're given a business problem and asked to design an analysis plan. Coding rounds focus on pandas, statistical modeling, and visualization. The strongest differentiator is how well you communicate insights to non-technical stakeholders during presentation rounds.
When evaluating opportunities: Good postings specify the data stack, the types of problems you'll work on, and the team structure. Look for companies that differentiate between analytics and ML data science. Vague 'data scientist' postings that list every skill under the sun usually mean the company doesn't know what they need.
AI Hiring Overview
The AI job market has 4,317 open positions tracked in our dataset. By seniority: 138 entry-level, 2,071 mid-level, 1,655 senior, and 453 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (635 positions). The remaining 3,657 roles require on-site or hybrid attendance.
The market median for AI roles is $215,000. Top-quartile compensation starts at $266,300. The 90th percentile reaches $320,790. Highest-paying categories: AI Safety ($287,500 median, 34 roles); Research Engineer ($272,100 median, 227 roles); AI Engineering Manager ($244,000 median, 23 roles).
Data Scientist roles remain in high demand, though the definition keeps shifting. Companies increasingly want candidates who can bridge traditional statistics with modern ML and LLM capabilities. The 'pure insights' data scientist role is consolidating into analytics engineering, while the 'build models' data scientist role is merging with ML engineering.
The AI Job Market Today
The AI job market spans 4,317 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (3,004), Data Scientist (345), AI Software Engineer (309). These three account for the majority of open positions, though smaller categories often have higher per-role compensation because of specialized skill requirements.
The seniority mix tells a story about where AI teams are in their maturity. Entry-level roles (138) are outnumbered by mid-level (2,071) and senior (1,655) positions, reflecting that most companies are past the 'build a team from scratch' phase and need experienced engineers who can ship production systems. Leadership roles (Director, VP, C-Level) total 453 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (635 positions), with 3,657 requiring on-site or hybrid attendance. The remote share has stabilized after the post-pandemic correction. Senior and specialized roles (Research Scientist, ML Architect) are more likely to be remote-eligible than entry-level positions, partly because experienced hires have more negotiating power and partly because these roles require less hands-on mentorship.
AI compensation is structured in clear tiers. The market median sits at $215,000. Top-quartile roles start at $266,300, and the 90th percentile reaches $320,790. These figures include base salary with disclosed compensation. Total compensation (including equity, bonuses, and sign-on) runs 20-40% higher at companies that offer those components.
Category matters for compensation. AI Safety roles lead at $287,500 median, while Prompt Engineer roles sit at $145,000. The spread between highest and lowest-paying categories reflects the premium on specialized technical skills versus broader analytical roles.
The most in-demand skills across all AI postings: Python (2,249 postings), Aws (1,224 postings), Azure (938 postings), Rag (915 postings), Gcp (660 postings), Pytorch (640 postings), Prompt Engineering (624 postings), Kubernetes (559 postings). Python dominates, appearing in the vast majority of role descriptions regardless of category. Cloud platform experience (AWS, GCP, Azure) is the second most common requirement. The newer entrants to the top skills list (RAG, vector databases, LLM APIs) reflect the shift from traditional ML toward generative AI applications.
Frequently Asked Questions
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