Excella

Data Scientist

• Lead the design and execution of data science projects, ensuring high-impact analytics solutions.
• Partner with client stakeholders to understand business challenges and develop data-driven strategies.
• Oversee data acquisition and integration, ensuring data quality and reliability.
• Apply and refine machine learning and predictive modeling techniques to solve complex business problems.
• Guide the deployment and integration of predictive models into business applications.
• Develop and implement strategies for optimizing model performance and monitoring over time.
• Present findings and recommendations to executives, translating complex data concepts into actionable business strategies.
• Mentor and support junior and senior team members in data science methodologies and best practices.
• Influence data governance and best practices within the organization.

Lead Consultant, Data Scientist

QA Automation Engineer

• Implement version control best practices, ensuring all application code, system configurations, and infrastructure code are stored in repositories.
• Develop deployment automation for infrastructure, applications, and security changes using modern scripting tools.
• Build and manage Continuous Integration (CI) pipelines to ensure rapid feedback on code changes.
• Apply trunk-based development strategies to maintain short-lived branches and streamlined workflows.
• Create and manage test automation suites for infrastructure and security features.
• Support test data management, collaborating with security, data science, and software development teams to optimize automated testing environments.
• Integrate security automation into DevOps pipelines, collaborating with InfoSec teams to implement preapproved security libraries and tools.
• Communicate technical concepts clearly to internal and external teams.
• Contribute to Agile team planning and execution of DevOps initiatives.

Senior DevOps Automation Engineer

• Lead the design and implementation of enterprise DevOps solutions, aligning with business goals.
• Define and enforce DevOps best practices across multiple teams, optimizing pipelines for scalability and efficiency.
• Architect and manage automated infrastructure deployment in cloud environments.
• Guide security integration into CI/CD pipelines, ensuring compliance with industry standards.
• Optimize test automation strategies to enhance system reliability and deployment confidence.
• Develop test data management solutions, ensuring high-quality data for automated testing.
• Influence tooling and technology choices for version control, CI/CD, and infrastructure automation.
• Provide mentorship and technical guidance to other DevOps Engineers.
• Communicate DevOps strategy and progress to stakeholders and clients.

Lead DevOps Automation Engineer

Security Engineer

• Lead efforts to gather and organize technical information about the program’s security posture
• Collaborate across teams to support ongoing security authorization
• Proactively create, monitor, and update POA&Ms
• Create Waivers or Risk Acceptance Memos
• Conduct annual assessments as per DHS guidelines
• Review and update security authorization documents annually
• Coordinate with Privacy, Records, and Information Governance Divisions
• Conduct Contingency Plan tests
• Perform system self-assessments
• Monitor and respond to ISVM and Patch Management requirements
• Provide audit support through all audit phases
• Maintain knowledge of inventory within the accreditation boundary
• Ensure security requirements are incorporated into development cycle
• Ensure configuration management processes are followed
• Respond to emerging requirements from legislation or policy changes
• Support annual independent assessments

Lead Information Systems Security Officer, ISSO

• Take ownership of the design and execution of data science projects, ensuring high-impact analytics solutions.
• Partner with client stakeholders to understand business challenges and develop data-driven strategies.
• Oversee data acquisition and integration, ensuring data quality and reliability.
• Apply and refine machine learning and predictive modeling techniques to solve complex business problems.
• Guide the deployment and integration of predictive models into business applications.
• Develop and implement strategies for optimizing model performance and monitoring over time.
• Present findings and recommendations to executives, translating complex data concepts into actionable business strategies.
• Mentor and support junior and senior team members in data science methodologies and best practices.
• Influence data governance and best practices within the organization.

Senior Consultant, Data Scientist

Analyst

• Collaborate with product leaders, designers, engineers, analysts, and business stakeholders to shape product direction and deliver valuable solutions from concept to launch.
• Define and evolve a product vision and strategy rooted in user needs, business objectives, and data.
• Lead discovery efforts, apply analysis techniques related to user research, mapping, and modeling, and conduct feasibility assessments to explore and validate product ideas.
• Create and maintain a product roadmap that balances short-term delivery with long-term innovation; ensure transparency and alignment across teams.
• Prioritize product features and capabilities based on value, effort, dependencies, and user feedback.
• Translate ideas into clear, testable user stories and success criteria; support agile events and iterative delivery.
• Establish and track key product metrics to evaluate performance and guide continuous improvement.
• Act as a bridge between business and technology, ensuring shared understanding of goals and outcomes.
• Support adoption planning and coordinate product rollouts using change management principles in collaboration with partners across the business.
• Proactively identify risks, blockers, and cross-team dependencies; take ownership of resolution paths.
• Help shape and evolve good practices across the team—encouraging collaboration, user focus, and agility.
• Mentor team members, share knowledge, and promote a product-thinking mindset across the organization.
• Lead and conduct functional testing, user acceptance testing (UAT), 508 compliance testing, etc.

Senior Product – Agile Analyst

• Define and drive product vision and strategy grounded in user research, data, and business goals; maintain a long-term roadmap while executing near-term priorities.
• Partner with executive stakeholders across business, engineering, design, and operations to align product initiatives with broader organizational strategy.
• Lead the end-to-end product lifecycle, from discovery and validation to delivery, adoption, and performance optimization.
• Drive cross-functional collaboration with UX designers, data specialists, developers, and SMEs to translate user needs into scalable solutions.
• Apply change management principles to guide adoption, readiness, and training for new product features and enterprise rollouts.
• Lead stakeholder management at all levels of the organization, facilitating alignment, gathering feedback, and influencing through storytelling and data.
• Oversee technical and functional requirements development, ensuring clarity, feasibility, and value alignment throughout the product lifecycle.
• Facilitate Agile events and scale Agile practices across teams, helping to evolve team maturity.
• Establish and track success metrics, customer satisfaction, and product health while recommending course corrections when needed.
• Support strategic decision-making with data-driven insights.
• Mentor and coach team members, helping develop a high-performing product culture and elevating analysis and delivery capabilities across the team or community of practice.

Lead Product – Agile Analyst

Data Analyst

• Develop a thorough understanding of the business context and objectives by eliciting and analyzing requirements from relevant stakeholders (business users, data engineers, data scientists, etc.)
• Operationalize business questions using data.
• Demonstrate understanding of relational databases, data architecture, and data modeling.
• Build complex SQL statements to answer business questions.
• Design conceptual data models, entity relationship diagrams, and business process models.
• Transform and cleanse data using tools such as R, SQL, or Python.
• Interpret, understand, and explain data insights to clients.
• Manage and create dashboards (drag and drop) for storytelling of regular information reports and ad-hoc requests.
• Build trust and respect, establish relationships, and develop rapport with technical and non-technical team members.
• Document Data Analysis processes and outcomes.
• Utilize industry or subject matter expertise (e.g., finance, employment, hospitality, web analytics).