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About This Role
Title: Sr. Data Scientist
Reports To: Director of Engineering
Department: Product \& Engineering
Location: Cincinnati, OH or Seattle, WA
Position Status: Salary Exempt
About DMG:
Divisions Maintenance Group provides facility maintenance services to retail chains and distribution and fulfillment centers across the country.
We are leading the way with our technology, creating world\-class products that are revolutionizing the industry and fulfilling our brand promise of “Uninterrupted Peace of Mind.”
DMG is a Certified Great Place to Work with a strong, inclusive culture and top\-notch benefits.
Job Summary:
We are currently building out a Marketplace Health and Pricing teams and as part of it building a Data Science practice, with the goal of better leveraging advanced analytic solutions. We believe analytics will be a game\-changer in the industries we operate in, and we are seeking a Data Scientist to help build and scale our data science and software engineering capabilities. At the outset, you will work on scaled and unique matching and pricing opportunities.
We are a fast\-paced, entrepreneurial team – this role will work across many different stakeholders to understand their needs and design solutions suitable for use across the industries we operate in. The ideal candidate will bring demonstrated experience in structured problem solving, model building and tuning, building production level analytics products, data collection \& cleansing, analytics execution, and communication of meaningful insights to diverse audiences of varying seniority and familiarity with analytic concepts. We have a fully cloud native stack built primarily on AWS.
What You'll Do:
- Partnering with stakeholders to translate complex business problems into data science and advanced analytics solutions.
- Write production level code for robust analytics products.
- Collaborate with data and software engineers to support data science solutions through the entire product lifecycle, including data wrangling, exploratory analysis, hypothesis testing, modeling, rapid prototyping, business validation and testing, and deployment.
- Leverage a diverse set of large and unstructured data to derive meaningful insights and information sets.
- Apply a variety of advanced analytical techniques including predictive modeling, machine learning, time series analysis, simulation, and optimization.
- Clearly and concisely synthesize and communicate findings to make thoughtful recommendations by combining business savvy with analytic rigor to technical and non\-technical audiences.
- Maintain expertise and awareness of emerging data science techniques, technologies, and potential business applications for AI/ML.
What You Need:
- Master’s degree in an analytical field such as Data Science, Computer Science, Applied Mathematics, Operations Research or Economics. 3 additional years of related experience may be substituted in lieu of a degree.
- 8\+ years of relevant data science or software engineering experience developing and deploying production models and writing production code for analytics products.
- Experience working with a variety of statistical and modeling techniques including hypothesis testing, supervised learning (classification and regression), forecasting, unsupervised clustering, and optimization.
- Experience with Python and SQL including building python packages.
- Experience gathering, interpreting, and translating business requirements into analytical solutions.
- Demonstrated ability to communicate complex analytical concepts and results at multiple levels to technical and non\-technical audiences.
- Experience with code version control platforms like GitHub, GitLab, or Azure DevOps
- Experience working with data science and analytics teams to develop complex analytics products that have been successfully delivered to customers
- Experience with large\-scale data wrangling using databases or Spark
- Knowledge of software engineering best practices for full software development life cycle, including coding standards, code reviews, source control management, continuous deployments, and testing
- Experience working with docker containers
- Experience working with APIs
- Experience working with a UI or web framework.
- Ability to manage the stress of a fast\-paced environment.
- Ability to meet the in\-person requirements of the team and/or business needs.
What You'll Get:
At DMG, you’ll be part of an amazing team that encourages learning, growth, and advancement. Our company has an entrepreneurial spirit that rewards self\-starters and encourages employees to take charge of their own careers.
Some of our many benefits include:
- Health, dental and vision coverage on day 1\.
- Dollar\-for\-dollar 401K match up to 4% of salary with immediate 100% vesting.
- Paid Primary and Secondary Caregiver leave.
- Employee Assistance Program to assist with everyday challenges.
- Paid time off to volunteer.
*Divisions Maintenance Group is an equal opportunity employer.*
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 3,823 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Divisions Maintenance Group, 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 $198,000 based on 808 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $227,400.
Across all AI roles, the market median is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. For comparison, the highest-paying categories include AI Engineering Manager ($275,000) and AI Safety ($274,200). By seniority level: Entry: $97,880; Mid: $165,000; Senior: $227,400; Director: $247,800; VP: $250,000.
Divisions Maintenance Group AI Hiring
Divisions Maintenance Group has 1 open AI role right now. They're hiring across Data Scientist. Based in US.
Location Context
AI roles in Austin pay a median of $215,300 across 523 tracked positions. That's 8% above the national 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 3,823 open positions tracked in our dataset. By seniority: 112 entry-level, 1,798 mid-level, 1,516 senior, and 397 leadership roles (Director, VP, C-Level). Remote roles make up 15% of the market (590 positions). The remaining 3,217 roles require on-site or hybrid attendance.
The market median for AI roles is $200,100. Top-quartile compensation starts at $253,500. The 90th percentile reaches $307,500. Highest-paying categories: AI Engineering Manager ($275,000 median, 41 roles); AI Safety ($274,200 median, 55 roles); Research Engineer ($260,000 median, 434 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 3,823 open positions across 15 role categories. The largest categories by volume: AI/ML Engineer (2,629), Data Scientist (322), AI Software Engineer (279). 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 (112) are outnumbered by mid-level (1,798) and senior (1,516) 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 397 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 15% of all AI roles (590 positions), with 3,217 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 $200,100. Top-quartile roles start at $253,500, and the 90th percentile reaches $307,500. 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 Engineering Manager roles lead at $275,000 median, while Prompt Engineer roles sit at $140,000. 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 (1,979 postings), Aws (1,190 postings), Azure (899 postings), Rag (839 postings), Gcp (726 postings), Pytorch (595 postings), Prompt Engineering (595 postings), Claude (540 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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