Director, Data Science and AIML

The selected candidate for this position will report on-site 2-3 days a week.

We are seeking an experienced and dynamic Director of Data Science and AI/ML to lead our data science AI and machine learning initiatives within a fast-paced data-driven organization. This role demands a blend of deep technical expertise in machine learning AI and data engineering combined with strong business acumen and strategic leadership. You will build and oversee a team of data scientists AI and machine learning engineers guiding end-to-end solution deliveryfrom ideation specification and model development to scalable deployment operationalization monitoring and continuous improvementprimarily leveraging Databricks.

As a hands-on leader you will balance strategic vision with direct technical involvement driving the implementation of robust MLOps and AIOps processes that ensure reliability efficiency and continuous improvement of AI/ML solutions in production. A key aspect of your role will be building trust and credibility with both business stakeholders and technical teams to foster collaboration alignment and shared success.

Duties and Responsibilities:

Leadership & Strategy

• Define and execute the vision and roadmap for data science AI and ML capabilities aligned with business objectives.

• Lead mentor and grow a high-performing team of data scientists and ML engineers fostering collaboration trust and continuous learning.

• Collaborate with cross-functional business leaders to identify prioritize and formalize AI/ML use cases and requirements producing both formal and evolving specification and operational documents.

• Establish best practices processes and governance for model development deployment and lifecycle management including AIOps and MLOps workflows.

• Lead User Acceptance Testing (UAT) processes ensuring delivered solutions meet business needs and quality standards.

• Champion iterative continuous improvement cycles balancing innovation with risk management and consistent value delivery.

• Build strong relationships and gain trust with business and technical teams by demonstrating transparency reliability and responsiveness.

Technical Oversight & Hands-on Contribution

• Oversee the end-to-end AIOPs & MLOps lifecycle on Databricks including data ingestion feature engineering model development validation deployment and continuous monitoring.

• Lead the design implementation and refinement of MLOps pipelines and AIOps frameworks on Databricks to automate experimentation deployment versioning monitoring and alerting of models in production.

• Implement infrastructure and tooling for automated model testing rollback and retraining triggered by performance degradation or data drift ensuring operational robustness and scalability.

• Ensure seamless collaboration between data scientists (focused on algorithm/model innovation and validation) and ML engineers (focused on scalable deployment optimization and MLOps infrastructure).

• Develop and enforce structured model handover contracts covering performance metrics latency memory footprint and operational constraints to ensure smooth transitions from development to production teams.

• Participate hands-on in performance optimization code reviews and troubleshooting of AI/ML solutions in production environments.

Collaboration & Communication

• Foster a culture of transparency and teamwork promoting close collaboration between data scientists AI/ML engineers software engineers and business stakeholders.

• Communicate complex technical concepts and AI/ML performance and value insights effectively to technical and non-technical audiences.

• Engage proactively with stakeholders to manage evolving requirements and expectations.

• Build and maintain trust with diverse teams by being approachable dependable and delivering on commitments.

• Manage stakeholder expectations and project timelines within a SAFe Agile environment while balancing innovation with operational excellence.

Innovation & Continuous Improvement

• Stay abreast of emerging trends and technologies in AI machine learning MLOps and AIOps applying them strategically to maintain competitive advantage.

• Demonstrate quick learning aptitude to continuously incorporate new tools methods and industry best practices while leveraging opportunities and managing risks.

• Drive continuous model refinement post-deployment leveraging live data and active learning techniques enabled by AIOps automation.

• Promote experimentation culture and data-driven decision making across the organization.

Skills:

Technical Skills

• Expertise in data science AI machine learning and deep learning with proficiency in Python SQL and ML/DL frameworks (e.g. Scikit-learn PyTorch TensorFlow).

• Hands-on experience with Databricks environment including Unity Catalog MLflow Mosaic AI/BI Spark Python.

• Experience working with Knowledge Graphs GraphRAG and AI Agents with MCP.

• Proven experience in designing and implementing MLOps pipelines covering experiment tracking model versioning CI/CD for ML deployment automation and model monitoring.

• Experience architecting and operationalizing AIOps processes for automated monitoring alerting anomaly detection and automated remediation of AI/ML system failures or data drift.

• Experience leading formal and evolving requirements gathering and translating them into actionable specifications.

• Demonstrated ability to lead User Acceptance Testing (UAT) and iterative continuous improvement cycles.

• Familiarity with model serving frameworks (TFServing TorchServe ONNX) and experiment tracking tools ( Weights & Biases).

Business & Leadership Skills

• Strong business acumen with the ability to translate complex business problems into enterprise grade solution implementations.

• Demonstrated ability to lead and inspire technical teams with experience managing cross-disciplinary groups.

• Excellent communication presentation and stakeholder management skills.

• Skilled at balancing strategic thinking with hands-on execution.

• Ability to foster a collaborative environment resolve conflicts effectively and gain trust from both business and technical teams.

• Quick learner with a strong awareness of emerging technologies associated risks and the imperative to deliver tangible business value.

Education & Experience Requirements:

Education:

• Bachelors Degree in Information Science Computer Science Engineering Mathematics Statistics or related STEM field.

Experience:

• 10 years of experience in data science machine learning or AI with at least three years in a leadership or managerial role.

• Proven track record of delivering AI/ML solutions in a production environment ideally within large-scale cloud and Databricks ecosystems.

• Experience implementing scalable and robust MLOps and AIOps processes and tooling including Knowledge Graphs and GraphRAG.

• Experience working across the full AI/ML lifecycle from model development to production deployment and monitoring.

• Previous experience bridging the gap between data science AI/ML and data engineering teams to create seamless workflows.

Preferred Requirements:

Education:

• Masters Degree in Information Science Computer Science Engineering Mathematics Statistics or related STEM field.

Experience:

• Passion for innovation continuous learning in AI/ML and operational excellence.

• Strong problem-solving mindset with a pragmatic approach.

• Ability to thrive in a dynamic fast-paced environment and manage multiple priorities.

• Experience with advanced analytics synthetic data generation and data annotation strategies is a plus.

All submissions should include a cover letter and resume.

The University of Maryland Global Campus (UMGC) is an equal opportunity employer and complies with all applicable federal and state laws regarding nondiscrimination. UMGC is committed to a policy of equal opportunity for all persons and does not discriminate on the basis of race color national origin age marital status sex sexual orientation gender identity gender expression disability religion ancestry political affiliation or veteran status in employment educational programs and activities and admissions.

Workplace Accommodations:

The University of Maryland Global Campus Global Campus (UMGC) is committed to creating and maintaining a welcoming and inclusive working environment for people of all abilities. UMGC is dedicated to the principle that no qualified individual with a disability shall based on disability be excluded from participation in or be denied the benefits of the services programs or activities of the University or be subjected to discrimination. For information about UMGCs Reasonable Workplace Accommodation Policy or to request an accommodation applicants/candidates can contact Employee Accommodations via email at.

Benefits Package Highlights:

• Generous Time Off:Enjoy 22 days of paid vacation 15 days of sick leave 3 personal days and 15 paid holidays (16 during general election years). For part-time employees time off rates will be prorated based on the number of hours worked.
• Comprehensive Health Coverage:Access to health care medical with vision dental and prescription plans for both individuals and families effective from the 1st of the month following your hire date.
• Insurance Options:Term Life Insurance Accidental Death and Dismemberment Insurance and Long-Term Disability (LTD) Insurance. Part-time employees working less than 0.5 FTE are not eligible for LTD.
• Flexible Spending Accounts:Available for medical and dependent care expenses.
• Retirement Plans:Choose between the Optional Retirement Program (ORP) or the Maryland State Retirement and Pension System (MSRPS).
• Supplemental Retirement Plans: include 401(k) 403(b) 457(b) and various Roth options. The university does not provide matching funds.
• Tuition Remission:Immediate availability for Regular Exempt Staff. Spouses and dependent children are eligible for undergraduate tuition remission after two years of service. NOTE: For part-time employees (at least 50 percent of the time) tuition remission benefits are prorated.

Hiring Range: