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About This Role
With a network of nearly 200 branches, Loomis armored transportation, cash management centers, and cash inventory vaults keep cash flowing throughout financial institutions and retail businesses across the US. Loomis prides itself on providing employees with opportunities for career advancement and job satisfaction. In fact, many of our company’s managers, vice presidents, and corporate executives started out in the branches as driver/guards and tellers. Our work can be challenging, but the thousands who have stayed with our company for decades will tell you that if you have the desire to learn and the drive to succeed, Loomis is the place to be. Come join our team!
Summary
The position of Data Scientist is for the Logicpath division within Loomis. We are a team of tech\-savvy cash inventory management experts passionate about helping financial institutions succeed.
We provide a collaborative and supportive environment that values the participation and contribution of all employees. We are looking for people who want to be challenged, solve complex problems, and feel connected to a larger purpose. Our mission\-focused team, collaborative nature, and commitment lead dedication to client results.
Function
The Data Scientist will play a critical role in designing, scaling, and operationalizing advanced analytics and machine learning solutions across the company’s FinTech platforms. This role will lead complex forecasting initiatives, develop AI\-driven use cases (including LLM\-enabled support tools), and establish strong data quality and model governance practices.
This position requires a hands\-on technical leader who can translate real\-world operational and financial problems into robust, production\-ready data science solutions, while partnering closely with engineering, product, implementation, and client\-facing teams.
The ideal candidate combines strong statistical and machine learning expertise with practical engineering ability and a track record of delivering production\-grade solutions in environments where communication, business processes, data quality, and operational constraints matter as much as model performance. This very technical person is capable of thinking in terms of “problem \- \> solution \- \> product \- \> value”, not just “models”.
Key Responsibilities
Forecasting \& Advanced Analytics
- Lead the design, development, and optimization of forecasting models for:
o Cash demand (branches, ATMs, retail locations, vaults)
o Labor and operational workload forecasting
- Apply and evaluate time\-series, probabilistic, and machine\-learning techniques to improve forecast accuracy and stability.
- Own model performance monitoring, drift detection, recalibration strategies, and continuous improvement.
AI, ML, \& LLM Enablement
- Design and implement LLM\-based use cases to support internal teams (e.g., support, implementation, operations).
- Develop approaches for prompt engineering, evaluation, and governance of LLM outputs.
- Partner with engineering to integrate AI capabilities into production SaaS workflows.
- Define metrics to measure effectiveness, accuracy, and operational impact (ROI) of AI solutions.
Data Quality, Governance \& Model Risk
- Establish data quality frameworks to detect anomalies, gaps, and integrity issues across large transactional datasets.
- Define validation rules, thresholds, and scoring mechanisms to support data confidence and forecast reliability.
- Contribute to model documentation, explainability, and governance practices aligned with financial services expectations.
- Support audit, compliance, and client due diligence inquiries related to data and models.
- Technical Leadership \& Collaboration
Required Qualifications
- 6\+ years of professional experience in data science, machine learning, or advanced analytics
- Advanced proficiency with Python and data science libraries (e.g., pandas, NumPy, scikit\-learn, TensorFlow/Torch)
- Strong SQL skills and experience working with messy, incomplete, high\-volume operational data
- Well\-rounded background in data science methods (e.g., supervised and unsupervised learning, anomaly detection, time series forecasting, survival analysis, simulation, optimization, causal analysis)
- Familiarity with metric design
- Demonstrated delivery of products that influenced business decisions
- Experience collaborating with engineering teams on model deployment and monitoring.
- Proven ability to communicate complex concepts clearly and effectively.
Preferred Qualifications
- Experience in FinTech, banking, payments, retail cash management, or operations
- Experience identifying high\-value data science opportunities in operational businesses
- Hands\-on LLM development experience
- Familiarity with data quality and model governance frameworks
Ideal Candidates are:
- Comfortable with ambiguity
- Driven to elevate themselves by elevating others
- Curious and life\-long learners
- Able to identify valuable problems before being asked
- Pragmatic rather than purely academically focused
- Capable of explaining very technical ideas to non\-technical stakeholders
- Willing to challenge their own and others’ assumptions with evidence
- Open to changing their mind when presented with new evidence
What Success Looks Like
- Forecasting models that are accurate, explainable, and trusted by clients and internal teams.
- AI and LLM use cases that measurably reduce operational effort and improve response quality.
- Strong data quality visibility that proactively identifies issues before they impact forecasts.
- Clear, well\-documented models and methodologies that scale across clients and use cases.
- A collaborative, high\-impact partnership with engineering, product, and client
Benefits:
Loomis offers one of the most comprehensive employee benefit packages in the industry, which includes:
- Vacation and Sick Time (PTO) as well as Paid Holidays
- Health \& Dental Insurance
- Vision Insurance
- 401(k) Plan
- Basic Life Insurance Plan
- Voluntary Life Insurance Plan
- Flexible Spending and Health Savings Account
- Dependent Care Account
- Industry\-leading Training and Development
Loomis is an Equal Opportunity Employer and Drug Free Workplace. Qualified applicants will receive consideration for employment without regard to their race, color, religion, national origin, sex, sexual orientation, gender identity, protected veteran status or disability.
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 3,708 AI roles we're tracking, Data Scientist positions make up 8% of the market. At Loomis, 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 463 positions with disclosed compensation. Mid-level AI roles across all categories have a median of $200,000.
Across all AI roles, the market median is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. For comparison, the highest-paying categories include AI Safety ($300,000) and Research Engineer ($280,000). By seniority level: Entry: $120,000; Mid: $200,000; Senior: $230,000; Director: $272,150; VP: $250,000.
Loomis AI Hiring
Loomis has 1 open AI role right now. They're hiring across Data Scientist. Based in Suwanee, GA, US.
Location Context
Across all AI roles, 14% (508 positions) offer remote work, while 3,180 require on-site attendance. Top AI hiring metros: New York (1,045 roles, $220,000 median); San Francisco (810 roles, $277,088 median); Los Angeles (397 roles, $215,000 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 3,708 open positions tracked in our dataset. By seniority: 102 entry-level, 1,705 mid-level, 1,469 senior, and 432 leadership roles (Director, VP, C-Level). Remote roles make up 14% of the market (508 positions). The remaining 3,180 roles require on-site or hybrid attendance.
The market median for AI roles is $217,500. Top-quartile compensation starts at $272,100. The 90th percentile reaches $325,000. Highest-paying categories: AI Safety ($300,000 median, 21 roles); Research Engineer ($280,000 median, 147 roles); AI Architect ($254,798 median, 67 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 3,708 open positions across 16 role categories. The largest categories by volume: AI/ML Engineer (2,605), Data Scientist (310), AI Software Engineer (259). 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 (102) are outnumbered by mid-level (1,705) and senior (1,469) 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 432 positions, representing the bottleneck between technical execution and organizational strategy.
Remote work availability sits at 14% of all AI roles (508 positions), with 3,180 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 $217,500. Top-quartile roles start at $272,100, and the 90th percentile reaches $325,000. 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 $300,000 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 (1,890 postings), Aws (1,103 postings), Azure (877 postings), Rag (855 postings), Gcp (631 postings), Prompt Engineering (560 postings), Pytorch (545 postings), Claude (498 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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