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About This Role
Primary Office Location:
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626 Washington Place. Pittsburgh, Pennsylvania. 15219\.
Join our team. Make a difference \- for us and for your future.
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Position Title: Senior Data Scientist
Business Unit: Strategy and Innovation
Reports to: Manager of Data Science
Position Overview:
This position leads the development and deployment of advanced analytics and machine learning solutions across fraud detection, document intelligence, customer analytics, and treasury forecasting. The Senior Data Scientist partners with business leaders to translate complex financial and operational problems into scalable models and AI solutions, with a focus on production deployment, model governance, and measurable business impact.
Primary Responsibilities:
Lead the design and development of machine learning models across use cases such as fraud detection, customer behavior modeling, document intelligence (OCR/Doc AI), and financial forecasting.
Partner with senior leadership and stakeholders to identify opportunities, define analytical approaches, and translate business needs into data\-driven solutions.
Architect and oversee end\-to\-end production ML systems, including data pipelines, feature stores, model deployment, and monitoring of model performance and drift.
Provide technical leadership, mentorship, and guidance to junior and mid\-level data scientists; review work to ensure quality, accuracy, and best practices.
Communicate complex analytical findings to both technical and non\-technical audiences; influence decision\-making through clear storytelling and recommendations.
Establish and maintain best practices in modeling, documentation, model governance, and performance monitoring to ensure scalability and compliance.
Design and evaluate experiments (e.g., A/B testing, back testing) to measure model impact on business outcomes such as fraud loss reduction, customer retention, and revenue growth.
Drive innovation by researching and applying new tools, techniques, and methodologies in data science and analytics.
Collaborate cross\-functionally with data engineering, IT, and business teams to ensure alignment and successful implementation of solutions.
Design and implement NLP, computer vision, and hybrid rule\-based/ML systems for document extraction, classification, and text analytics use cases.
Performs other related duties and projects as assigned.
All employees have the responsibility and the accountability to serve as risk managers for their businesses by understanding, reporting, responding to, managing and monitoring the risk they encounter daily as required by F.N.B. Corporation’s risk management program.
F.N.B. Corporation is committed to achieving superior levels of compliance by adhering to regulatory laws and guidelines. Compliance with regulatory laws and company procedures is a required component of all position descriptions.
Minimum Level of Education Required to Perform the Primary Responsibilities of this Position:
BA or BS
Minimum \# of Years of Job Related Experience Required to Perform the Primary Responsibilities of this Position:
5
Skills Required to Perform the Primary Responsibilities of this Position:
Excellent communication skills, both written and verbal
Excellent organizational, analytical and interpersonal skills
Detail\-oriented
Excellent project management skills
MS Excel \- Intermediate Level
MS PowerPoint \- Intermediate Level
Ability to use a personal computer and job\-related software
Excellent management skills
Python (pandas, scikit\-learn, etc.)
SQL (advanced querying, data extraction)
Machine Learning (supervised/unsupervised)
Statistical modeling \& experimentation
Data Wrangling \& Feature Engineering
Licensures/Certifications Required to Perform the Primary Responsibilities of this Position:
N/A
Physical Requirements or Work Conditions Beyond Traditional Office Work:
N/A
Equal Employment Opportunity (EEO):
It is the policy of F.N.B. Corporation (FNB) and its affiliates not to discriminate against any employee or applicant for employment because of age, race, color, religion, sex, national origin, disability, veteran status or any other category protected by law. It is also the policy of FNB and its affiliates to employ and advance in employment all persons regardless of their status as individuals with disabilities or veterans, and to base all employment decisions only on valid job requirements. FNB provides all applicants and employees a discrimination and harassment free workplace.
FNB will not provide sponsorship for employment\-based visas for this position; only candidates who are legally authorized to work in the U.S. will be considered.
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 First National 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 463 positions with disclosed compensation. Senior-level AI roles across all categories have a median of $230,000.
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.
First National Bank AI Hiring
First National Bank has 1 open AI role right now. They're hiring across Data Scientist. Based in Pittsburgh, PA, 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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