Sr Director, Data Science

Business Processing Solutions•Philadelphia, PA

4h•Remote

About The Position

The Sr. Director, Data Science is Duncan Solutions' enterprise leader for artificial intelligence, machine learning, and advanced analytics — responsible for transforming data from a reporting asset into a strategic one. This role defines how data-driven intelligence is applied across the business: in client outcomes, operational performance, pricing, workforce planning, and the decisions that determine where Duncan grows next. Reporting to the CTO, this role operates at the intersection of technology and enterprise strategy. The Director does not simply manage a data science function — this role sets the vision for what AI-enabled decision-making looks like at Duncan, builds the capability to execute it, and holds the organization accountable for realizing measurable financial and operational returns on every analytics investment. This role is accountable for the full data science lifecycle: from model development and production deployment through governance, explainability, and ethical AI practice. It is also accountable for talent — recruiting, developing, and retaining the analytics professionals who will power this capability at scale.

Requirements

Proven ability to translate complex technical solutions into measurable operational and financial outcomes — communicated clearly to executive and non-technical audiences
Demonstrated experience building executive-level dashboards and data visualizations that enable informed decision-making across an organization
Strong executive presence with demonstrated ability to influence strategy and decision-making at the C-suite level and, where appropriate, with board audiences
Proven experience establishing AI governance frameworks — including model validation, explainability, monitoring, and ethical AI standards
Strong financial and business acumen; demonstrated ability to align innovation investments to enterprise value creation with documented ROI discipline
Experience leading, developing, and retaining high-performing technical teams in a multi-functional, matrixed environment
Exceptional written and verbal communication — clear, direct, and credible with both technical and non-technical audiences
High school diploma or GED equivalent
Minimum 8+ years of progressive experience in data science, analytics, or machine learning, including a minimum of 3+ years in a leadership role
Demonstrated success deploying machine learning models in production within AWS-native environments (SageMaker, Redshift, QuickSight, Databricks, Lambda, or equivalent)
Strong programming proficiency in Python, R, and SQL; experience with machine learning frameworks and model development tools

Nice To Haves

Bachelor's or Master's degree in Data Science, Computer Science, Statistics, Applied Mathematics, or related quantitative field; PhD is a plus
8–12 years of progressive data science and analytics leadership experience within a technology-enabled, services-based, or PE-backed organization
Experience building or scaling analytics capabilities within a growth-oriented or carve-out environment where infrastructure and team capability are being established concurrently
Demonstrated understanding of how data science investments translate into client value — particularly within transportation management, parking and tolling, citation processing, or related service verticals
Proven ability to facilitate high-stakes analytics and technology campaigns designed and optimized for strategic growth
Demonstrated sound judgment for handling sensitive data, model outputs, and organizational risk findings with the ability to navigate ambiguity responsibly
Track record of holding high standards while protecting the conditions that make sustained technical performance possible — not treating rigor and pace as competing priorities
Experience closing the loop — from problem identification and model development, through deployment, to documented business impact

Responsibilities

Define and lead the enterprise AI and data science strategy — aligned to Duncan's Enterprise Goals, corporate growth objectives, and the imperative to deliver client value through our products and services
Advise the CTO and executive leadership team on opportunities to leverage artificial intelligence, machine learning, and predictive analytics to drive competitive advantage, margin improvement, and operational scale
Translate strategic business priorities into high-impact data science programs with clearly defined financial and operational outcomes — not exploration for its own sake, but applied innovation with measurable returns
Evaluate emerging technologies and industry trends to inform long-term digital strategy and investment decisions, bringing forward evidence-based recommendations rather than reactive responses to market noise
Oversee the design, deployment, and scaling of advanced analytics solutions within AWS-native environments — supporting revenue optimization, compliance oversight, customer experience enhancement, and operational efficiency
Champion automation and intelligent workflow transformation, including natural language processing, anomaly detection, and AI-driven operational optimization across Duncan's core service lines
Partner with Finance, Operations, Technology, Sales, and Business Intelligence leaders to integrate predictive insights into forecasting, workforce planning, pricing strategy, and client performance management
Ensure analytics solutions are production-ready, scalable, and maintained with the operational rigor expected of enterprise systems — not prototype-quality deliverables deployed into live environments
Establish and maintain governance frameworks for model development, validation, monitoring, explainability, and ethical AI use — ensuring regulatory alignment and enterprise risk management at every stage of the analytics lifecycle
Ensure responsible AI practices are embedded in team culture and delivery methodology — not treated as compliance overhead, but as a professional standard that protects both Duncan and the clients it serves
Identify and surface emerging data science risks — model drift, data quality degradation, bias exposure, or governance gaps — before they become material issues for the enterprise or its clients
Partner with Legal, Compliance, and the CTO on AI-related regulatory developments, ensuring Duncan's practices remain defensible, auditable, and ahead of evolving requirements
Build and maintain executive-level dashboards and reporting frameworks that convert data science outputs into decision-enabling intelligence — giving the CTO, CEO, and enterprise leadership a clear, timely view of operational performance and AI-driven opportunity
Ensure data science insights are communicated in plain, accessible language that enables non-technical leaders to act with confidence — translating model outputs into business narratives, not statistical summaries
Support board-level reporting on AI strategy, innovation initiatives, and enterprise performance impact as directed by the CTO
Partner with the HR Analyst and Talent Development Lead to ensure workforce analytics and people data are applied with precision and integrity across enterprise planning cycles
Recruit, develop, and retain high-performing data science and analytics professionals — building a team culture defined by accountability, intellectual rigor, and continuous improvement
Establish clear performance expectations, development pathways, and capability-building investments aligned to Duncan's Job Group Family Architecture and the career development framework being built enterprise-wide
Ensure the data science team operates as a trusted enterprise partner — responsive to business needs, disciplined in delivery, and consistent in the quality of its work
Model the leadership behaviors Duncan expects at the senior leader level: operating with independence, developing the people around you, and connecting your team's daily work to the organization's long-term direction