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About This Role
Value Proposition:
Our values define us and our culture inspires us to change lives for the better. Our employees are the heart and soul of our company, and every success we experience begins with them. Together we are committed to making a positive impact in our local communities. We champion a culture of continuous learning, work\-life integration, and inclusion. We promote a digitally enabled work environment to continuously enhance the experience of our employees and customers.
Overview:
This is a full\-time career opportunity that can be remote.
The Data Scientist I will be responsible for the end\-to\-end process of analyzing data, from collecting and cleaning raw data to building and deploying insights, analysis, and standard predictive models, and communicating the results to stakeholders . Key responsibilities include performing exploratory data analysis, developing and refining machine learning models, and collaborating with cross\-functional teams to implement data\-driven solutions and provide actionable business insights and analysis. This role will support higher\-level data scientists and leadership with their work on complex models by completing "upfront" data work and research required for the models.
Responsibilities:
- Identify and collect data from various sources, such as databases (on\-premise and cloud), surveys, and other mediums.
- Clean and preprocess large, raw, and structured or unstructured datasets to ensure data quality, accuracy, and usability.
- Conduct exploratory “upfront” data analysis (EDA) to find patterns, trends, and anomalies, and make appropriate changes for final analysis or model development.
- Work in close collaboration with higher\-level data scientists to develop and implement algorithms and machine learning models to solve business problems and make predictions.
- Test and refine models for accuracy and improved performance.
- Evaluate the performance of models using relevant metrics and monitor for degradation.
- Translate complex findings into clear, actionable insights for stakeholders.
- Create reports, dashboards, and visualizations to communicate results effectively.
- Collaborate with developers and engineers to deploy models into production.
- Work with cross\-functional teams to integrate data\-driven solutions into business strategies.
- Continuously seek out industry best practice and skills development to create new capabilities for data analytics and data science.
- Ad\-hoc projects \& analysis assigned by management or senior leadership.
#### Additional Responsibilities
- Project management
- Utilize publicly available machine learning or deep learning tools such as TensorFlow, PyTorch, Keras, Scikit\-learn, etc. in pursuit of business objectives
- Keep pace with developments in the data science field
Qualifications:
#### Education
Bachelor's Degree or the equivalent experience. Specialty: applied math, statistics, computer science, engineering, econometrics, or related field. (Required)
Master's Degree or the equivalent experience. Specialty: applied math, statistics, computer science, engineering, econometrics, or related field. (Required)#### Experience
2 or more years data mining, machine learning algorithms, large datasets and complex relational data models. (Required)
3 or more years proficient programming with Python. (Preferred)
2 or more years Scikit\-learn, Keras, Pandas, Numpy. (Preferred)
1 or more years cloud computing. (Preferred)
1 or more years financial services. (Preferred)#### Knowledge, Skills, and Abilities
- Working knowledge of various supervised and unsupervised learning methods such as logistic regression, bagging \& boosting, clustering, etc. (Required)
- Working knowledge of machine learning learning techniques, applications, and libraries. (Required)
- Hands\-on experience with AWS, GCP, or Azure. Able to utilize API endpoints. (Preferred)
- Effective communications skills and ability to articulate recommendations to varied audiences (Required)
Other Duties as Assigned by Manager:
This role may perform other job duties as assigned by the manager. Each employee of the Organization, regardless of position, is accountable for reading, understanding and acting on the contents of all Company\-assigned and/or job related Compliance Programs, regulations and policies and procedures, as well as ensure that all Compliance Training assignments are completed by established due dates. This includes but is not limited to, understanding and identifying compliance risks impacting their department(s), ensuring compliance with applicable laws or regulations, and escalating compliance risks to the appropriate level of management.
Pay Transparency:
To provide greater transparency to candidates, we share base salary ranges on all job postings regardless of state. We set standard salary ranges for our roles based on the position, function, and responsibilities, as benchmarked against similarly sized companies in our industry. Specific compensation offered will be determined based on a combination of factors including the candidate’s knowledge, skills, depth of work experience, and relevant licenses/credentials. The salary range may vary based on geographic location.
The salary range for this position is $72,500\.00 \- $120,800\.00 annually. Additional Compensation Components
This job is eligible to participate in a short\-term incentive compensation plan subject to individual and company performance.
Benefits:
Additionally, as part of our Total Rewards program, Fulton Bank offers a comprehensive benefits package to those who qualify. This includes medical plans with prescription drug coverage; flexible spending account or health savings account depending on the medical plan chosen; dental and vision insurance; life insurance; 401(k) program with employer match and Employee Stock Purchase Plan; paid time off programs including holiday pay and paid volunteer time; disability insurance coverage and maternity and parental leave; adoption assistance; educational assistance and a robust wellness program with financial incentives. To learn more about your potential eligibility for these programs, please visit Benefits \& Wellness \| Fulton Bank.
EEO Statement: Fulton Bank (“Fulton”) is an equal opportunity employer and is committed to providing equal employment opportunity for all qualified persons. Fulton will recruit, hire, train and promote persons in all job titles, and ensure that all other personnel actions are administered, without regard to race, color, religion, creed, sexual orientation, national origin, citizenship, gender, gender identity, age, genetic information, marital status, disability, covered veteran status, or any other legally protected status. Sponsorship Statement:
As a condition of employment, individuals must be authorized to work in the United States without sponsorship for a work visa by Fulton Bank currently or in the future.
Salary Context
This $72K-$120K range is in the lower quartile 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 Fulton Bank, 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. Mid-level AI roles across all categories have a median of $194,400. This role's midpoint ($96K) sits 50% below the category median. Disclosed range: $72K to $120K.
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.
Fulton Bank AI Hiring
Fulton Bank has 1 open AI role right now. They're hiring across Data Scientist. Based in Lancaster, PA, US. Compensation range: $120K - $120K.
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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