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Data Scientist - Job Opportunity at Valley National Bank

Morristown, United States
Full-time
Mid-level
Posted: June 14, 2025
On-site
USD 83,100 - 143,500 per year

Benefits

Competitive salary structure with transparent pay ranges demonstrating commitment to fair compensation practices
Employment with an established regional bank providing stability and career security in the financial services sector
Opportunity to work in a data-driven culture with mentorship responsibilities enhancing leadership development
Access to advanced analytics tools and banking systems providing cutting-edge technical experience

Key Responsibilities

Drive strategic business impact by independently translating complex business requirements into actionable data analysis, reports, and executive dashboards that directly influence senior management decisions
Lead cross-functional communication initiatives by presenting technical findings to non-technical stakeholders, ensuring data insights drive organizational strategy and business outcomes
Pioneer new data capabilities and strategic initiatives by identifying opportunities for data-driven solutions and collaborating with technical teams to implement enterprise-wide improvements
Establish autonomous business partnerships by independently managing stakeholder relationships, responding to critical data requests, and serving as the primary data consultant for business units
Execute high-impact data analysis projects involving complex queries, data reconciliation, and strategic recommendations that shape senior management decisions and business direction
Cultivate organizational data maturity by mentoring junior analysts, building internal data capabilities across departments, and establishing best practices that strengthen the company's data-driven culture

Requirements

Education

Bachelor's degree in a quantitative discipline, such as Business Analytics, Data Science, Mathematics, Econometrics, Engineering, Sciences

Experience

Minimum of 3 years working in a data analytics role with hands-on experience in wrangling large data sets, conducting data analysis and developing insights from data

Required Skills

Hands-on experience in data modelling, data engineering and data analysis tools such as BI tools, Power BI, SQL, Python (pandas, numpy) Working knowledge in developing reports and dashboards in Power BI or Tableau Strong communication, organizational and interpersonal skills, as well as the ability to prioritize and execute multiple objectives Ability to identify key insights and present results to a business audience Preferred experience in mapping techniques, such as calculating distances and routes, as well as ability to build custom market and demographic maps Awareness of latest trends in data analytics Knowledge and experience in the banking industry and banking systems Advanced knowledge of data structures and the ability to visualize data effectively to communicate to a business audience Strong collaboration and communication skills to effectively partner with other data teams and business partners
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Sauge AI Market Intelligence

Industry Trends

The banking industry is experiencing a significant digital transformation wave, with traditional regional banks like Valley National Bank investing heavily in data analytics capabilities to compete with fintech companies and maintain market relevance. This trend is driving increased demand for data scientists who can bridge the gap between traditional banking operations and modern data-driven decision making. Financial services organizations are increasingly prioritizing customer experience optimization through advanced analytics, requiring data scientists to develop sophisticated models for customer segmentation, product recommendations, and risk assessment. This shift represents a move from reactive reporting to predictive analytics that can drive proactive business strategies. Regulatory compliance in banking is becoming more data-intensive, with institutions needing advanced analytics capabilities to monitor transactions, assess risk, and ensure compliance with evolving regulations. Data scientists in banking are increasingly responsible for developing automated compliance monitoring systems and risk assessment models. The integration of geospatial analytics in banking is emerging as a competitive advantage, particularly for commercial lending, branch optimization, and market analysis. The emphasis on mapping techniques in this role reflects the industry's growing recognition of location-based insights for strategic decision-making.

Salary Evaluation

The offered salary range of $83,100 - $143,500 is competitive for a mid-level data scientist position in the New Jersey market, particularly considering the banking industry's traditionally conservative but stable compensation structure. This range aligns well with regional market rates and reflects the premium that financial services companies pay for candidates with relevant industry experience.

Role Significance

Typical data science teams in regional banks of Valley National's size range from 5-15 professionals, with this role likely supervising 2-4 junior analysts while reporting to a Chief Data Officer or Analytics Director. The mentorship component suggests this position serves as a bridge between senior leadership and junior team members.
This mid-level data scientist role carries significant organizational influence, with direct access to senior management and autonomous decision-making authority. The position represents a strategic contributor level rather than an individual contributor role, evidenced by the mentorship responsibilities and independent business partner interaction requirements.

Key Projects

Development of customer lifetime value models and segmentation strategies that directly impact marketing spend allocation and customer acquisition strategies Implementation of risk assessment models for commercial and retail lending that influence credit decisions and portfolio management Creation of branch performance analytics and market penetration studies that guide expansion and consolidation decisions Design of regulatory compliance monitoring systems that automate risk detection and reporting processes

Success Factors

Technical versatility across multiple analytics platforms combined with deep understanding of banking regulations and financial products, enabling the translation of complex regulatory requirements into actionable technical solutions Executive communication skills that can effectively distill complex analytical findings into strategic recommendations for senior management, particularly in an industry where technical literacy varies significantly among decision-makers Business acumen specific to banking operations, including understanding of credit risk, customer lifecycle management, and regulatory compliance requirements that impact every analytical project Leadership and mentorship capabilities that can elevate team performance while building organizational data literacy across traditionally non-technical banking departments

Market Demand

High demand exists for data scientists with banking industry experience, as financial institutions face increasing pressure to modernize their analytics capabilities while maintaining regulatory compliance. The combination of technical skills and financial services knowledge creates a relatively scarce talent pool, giving qualified candidates significant leverage in the job market.

Important Skills

Critical Skills

Python programming with pandas and numpy libraries forms the technical foundation for data manipulation, analysis, and model development in the banking environment where large datasets and complex calculations are standard requirements SQL database querying skills are essential for extracting and manipulating data from core banking systems, regulatory databases, and customer information systems that form the backbone of financial institution operations Power BI and Tableau expertise enables the creation of executive dashboards and business intelligence solutions that translate complex analytical findings into actionable insights for senior management and business stakeholders Banking industry knowledge and systems experience provide crucial context for understanding regulatory requirements, risk management principles, and business processes that influence every analytical project in the financial services sector

Beneficial Skills

Advanced statistical modeling and machine learning capabilities would enhance the ability to develop predictive models for credit risk, customer behavior, and market analysis that can provide competitive advantages Project management and stakeholder management skills would support the autonomous business partner interaction requirements and enable more effective cross-functional collaboration Regulatory knowledge specific to banking compliance, risk management, and reporting requirements would accelerate project delivery and ensure analytical solutions meet industry standards Leadership and team development skills would enhance the mentorship responsibilities and position the candidate for advancement into management roles

Unique Aspects

The specific emphasis on geospatial analytics and mapping capabilities distinguishes this role from typical banking data scientist positions, suggesting involvement in sophisticated location-based analysis for commercial lending, market expansion, and competitive intelligence
The autonomous business partner interaction requirement indicates an unusually high level of independence and business responsibility for a mid-level role, suggesting accelerated career development opportunities
The combination of technical depth requirements and mentorship responsibilities creates a unique hybrid role that bridges individual contribution with team leadership
The transparent salary disclosure and emphasis on fair compensation practices indicates a progressive approach to talent acquisition that may reflect broader organizational modernization efforts

Career Growth

Career progression typically occurs within 2-4 years for high-performing mid-level data scientists in banking, with advancement accelerated by successful project delivery, team leadership demonstration, and development of specialized expertise in areas like risk modeling or regulatory analytics.

Potential Next Roles

Senior Data Scientist or Principal Data Scientist roles within 2-3 years, focusing on advanced modeling and strategic analytics initiatives Data Science Manager or Analytics Director positions, overseeing larger teams and multiple analytics workstreams across the organization Chief Data Officer roles at smaller financial institutions, or senior leadership positions in data strategy at larger banks Consultant or advisor roles serving multiple financial institutions, leveraging deep industry expertise and technical skills

Company Overview

Valley National Bank

Valley National Bank operates as a regional financial institution with a strong presence in the New York and New Jersey markets, serving both commercial and retail customers through traditional banking services while investing in digital transformation initiatives. The bank has been expanding its geographic footprint and service offerings, requiring sophisticated analytics capabilities to support growth strategies and competitive positioning.

As a publicly-traded regional bank with assets exceeding $40 billion, Valley National Bank occupies a solid position in the competitive banking landscape, large enough to invest in advanced analytics capabilities while maintaining the agility to implement new technologies more quickly than larger multinational banks.
The Morristown, New Jersey location positions this role at the heart of the bank's operational center, providing access to senior leadership and core business operations while serving a affluent market demographic that generates significant commercial and retail banking opportunities.
Valley National Bank maintains a traditional banking culture emphasizing relationship-building and customer service, while actively modernizing its approach to data and analytics. The emphasis on mentorship and collaboration in this role reflects a culture that values knowledge sharing and professional development within a stable, established organization.
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