Senior Manager, Data Science

Jobtailor; Charlotte (NC); On-site; USD 140,000 - 190,000; Full time

Job summary

Jobtailor is seeking a senior leader to guide a data science organization in Charlotte, US. You will coach a team, partner with executives to identify high-value AI opportunities, and drive end-to-end analytics initiatives across the enterprise.

You will oversee governance, model development, deployment, and monitoring while advocating responsible AI and regulatory compliance. Expect collaboration with risk, tech, and business units.

Qualifications

• Undergraduate degree or advanced technical degree preferred.
• Graduate's degree preferred with progressive project work experience, or 7+ years of relevant experience.
• 3+ years deep expertise in machine learning algorithms, statistical modeling, predictive analytics, and experimentation methodologies.
• 7+ years of experience applying advanced analytics, ML, AI, or statistical modeling techniques in complex business environments.
• Advanced proficiency in Python and common data science libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar frameworks.
• Experience developing and deploying Generative AI, Large Language Model (LLM), Retrieval-Augmented Generation (RAG), or Agentic AI solutions
• Experience operationalizing machine learning models within enterprise environments using MLOps practices and cloud platforms
• Strong understanding of model governance, explainability, fairness, bias mitigation, and responsible AI practices
• Experience leading cross-functional AI and analytics initiatives involving business, technology, risk, and governance stakeholders
• Demonstrated ability to communicate complex analytical concepts to executive and non-technical audiences
• Experience in financial services, banking, risk management, audit, regulatory, or highly regulated industries
• Familiarity with cloud-based analytics environments such as Azure, AWS, or Google Cloud
• Experience managing a portfolio of data science initiatives and delivering measurable business value
• Occasional domestic travel required
• Must perform sedentary work, operate standard office equipment, sit, and concentrate for long periods continuously

Responsibilities

• Lead and develop a team of data scientists through coaching, mentorship, technical guidance, and career development.
• Partner with business leaders to identify and prioritize high-value machine learning, AI, and analytics opportunities.
• Translate complex business problems into analytical frameworks, models, and actionable insights.
• Oversee end-to-end data science lifecycle from problem formulation to deployment and monitoring.
• Guide predictive, prescriptive, and generative AI solutions to improve business performance and risk management.
• Collaborate with data engineering, technology, and platform teams on production-ready solutions.
• Establish model governance, validation, explainability, documentation, and monitoring practices.
• Evaluate emerging AI, ML, and data science techniques and recommend applications.
• Present analytical findings, recommendations, and business cases to senior executives and stakeholders.
• Drive experimentation and innovation through proofs of concept, pilots, and test-and-learn initiatives.
• Ensure responsible and ethical use of AI in accordance with regulatory requirements, governance frameworks, and internal policies.
• Manage portfolio planning, resource allocation, and delivery execution across concurrent initiatives.
• Hire talent, set goals, develop staff, manage performance and compensation decisions, and handle disciplinary actions as required.
• Oversee a large and/or highly complex analytical function.
• Partner with leadership on portfolio and financial management, strategic roadmaps, and long-term goals.
• Lead enterprise analytics solutions for customers and collaborate with business partners on ad hoc analysis.
• Manage team workload, assign data requests, develop business plans, identify growth opportunities, and report risk issues.
• Maintain alignment with enterprise frameworks, regulatory requirements, controls, remediation plans, and risk appetite.
• Build and retain an engaged, diverse, inclusive, and high-performing team.
• Develop annual and long-term plans aligned with enterprise priorities.

Skills

Machine Learning Algorithms

Advanced Analytics

Python Proficiency

Model Governance

Generative AI Solutions

Education

Undergraduate degree or advanced technical degree preferred

Graduate's degree preferred

7+ years relevant experience

Tools

Pandas

NumPy

Scikit-learn

TensorFlow

PyTorch

XGBoost

Azure

AWS

Google Cloud

Data Engineering Tools

Job description

• Lead and develop a team of Data Scientists through coaching, mentorship, technical guidance, and career development
• Partner with business leaders to identify and prioritize high-value machine learning, artificial intelligence, and advanced analytics opportunities
• Translate complex business problems into analytical frameworks, models, and actionable insights
• Oversee the end-to-end data science lifecycle, including problem formulation, feature engineering, model development, validation, deployment, monitoring, and optimization
• Guide predictive, prescriptive, and generative AI solutions to improve business performance, efficiency, customer experience, and risk management
• Collaborate with data engineering, technology, and platform teams on scalable, production-ready solutions
• Establish model governance, validation, explainability, documentation, and monitoring practices
• Evaluate emerging AI, machine learning, and data science techniques and recommend applications
• Present analytical findings, recommendations, and business cases to senior executives and stakeholders
• Drive experimentation and innovation through proofs of concept, pilots, and test-and-learn initiatives
• Ensure responsible and ethical use of AI in accordance with regulatory requirements, governance frameworks, and internal policies
• Manage portfolio planning, resource allocation, and delivery execution across concurrent initiatives
• Hire talent, set goals, develop staff, manage performance and compensation decisions, and handle disciplinary actions as required
• Oversee a large and/or highly complex analytical function
• Partner with leadership on portfolio and financial management, strategic roadmaps, and long-term goals
• Lead enterprise analytics solutions for customers and collaborate with business partners on ad hoc analysis
• Manage team workload, assign data requests, develop business plans, identify growth opportunities, and report risk issues
• Maintain alignment with enterprise frameworks, regulatory requirements, controls, remediation plans, and risk appetite
• Build and retain an engaged, diverse, inclusive, and high-performing team
• Develop annual and long-term plans aligned with enterprise priorities

Requirements

• Undergraduate degree or advanced technical degree preferred
• Graduate's degree preferred with either progressive project work experience, or 7+ year of relevant experience; higher degree education and research tenure can be counted
• 3+ years deep expertise in machine learning algorithms, statistical modeling, predictive analytics, and experimentation methodologies
• 7+ years of experience applying advanced analytics, machine learning, artificial intelligence, or statistical modeling techniques in complex business environments
• Advanced proficiency in Python and common data science libraries such as Pandas, NumPy, Scikit-learn, TensorFlow, PyTorch, XGBoost, or similar frameworks
• Experience developing and deploying Generative AI, Large Language Model (LLM), Retrieval-Augmented Generation (RAG), or Agentic AI solutions
• Experience operationalizing machine learning models within enterprise environments using MLOps practices and cloud platforms
• Strong understanding of model governance, explainability, fairness, bias mitigation, and responsible AI practices
• Experience leading cross-functional AI and analytics initiatives involving business, technology, risk, and governance stakeholders
• Demonstrated ability to communicate complex analytical concepts to executive and non-technical audiences
• Experience in financial services, banking, risk management, audit, regulatory, or highly regulated industries
• Familiarity with cloud-based analytics environments such as Azure, AWS, or Google Cloud
• Experience managing a portfolio of data science initiatives and delivering measurable business value
• Occasional domestic travel required
• Must perform sedentary work, operate standard office equipment, sit, and concentrate for long periods continuously

Core Competencies

Demonstrates expertise in machine learning, artificial intelligence, and advanced analytics, with a strong focus on model governance, ethical AI practices, and delivering business value through data-driven insights. Proven ability to lead and develop high-performing teams while collaborating with cross-functional stakeholders to drive innovation and operational excellence.

Highest-signal resume keywords

• Machine Learning Algorithms
• Advanced Analytics
• Python Proficiency
• Model Governance
• Generative AI Solutions

Hard Skills

• Statistical Modeling
• Predictive Analytics
• Experimentation Methodologies
• Feature Engineering
• MLOps Practices
• Data Science Libraries
• Cloud Platforms
• Data Science Lifecycle Management
• Analytical Frameworks
• Portfolio Management

Soft Skills

• Coaching
• Mentorship
• Communication
• Collaboration
• Leadership

Industry Keywords

• Financial Services
• Risk Management
• Regulatory Compliance
• Governance Frameworks
• Business Analytics

Tools & Technologies

• Pandas
• NumPy
• Scikit-learn
• TensorFlow
• PyTorch
• XGBoost
• Azure
• AWS
• Google Cloud
• Data Engineering Tools