• Lead end‑to‑end workforce planning for Provider Enablement functions, including long‑term forecasting, short‑term planning, and ongoing resource management • Develop and maintain capacity models to support Claim Enablement programs, including quality audit activities, training, coaching, and operational readiness • Translate business demand, productivity assumptions, and program requirements into actionable staffing and capacity plans • Partner with Enablement and Operations leaders to anticipate demand changes driven by program expansion, regulatory requirements, or operational strategy • Provide oversight of short‑term and monthly capacity planning cycles, including variance analysis and risk identification • Manage resource allocation across multiple enablement work types, ensuring alignment to priorities, skill requirements, and service objectives • Lead the expansion of workforce planning capabilities across broader Provider Enablement, including Provider Call and Network Operations • Deliver clear insights and recommendations on staffing risks, trade‑offs, and mitigation strategies • Lead and develop workforce planning team members supporting Enablement functions • Drive continuous improvement of planning models, processes, and tools to improve accuracy, transparency, and agility • Partner with Finance to support budget planning, forecasting, and financial impact assessments related to staffing changes • Participate in operational and planning forums, providing workforce planning expertise to support decision‑making

• Leading forecasting, short‑term planning, scheduling, and real‑time workforce management for Provider Call Operations • Ensuring staffing strategies are aligned to demand, service level objectives, and financial targets • Partnering closely with Operations, Finance, and Global Service Partners to develop forecasts • Translating long‑range forecasts into actionable short‑term staffing and capacity plans • Analyzing forecast accuracy, identifying variance drivers, and implementing improvements • Overseeing schedule development and optimization to meet service, productivity, and cost targets • Ensuring schedules account for shrinkage, skill mix, attrition, training, and operational constraints • Leading real‑time management strategy and intraday execution to respond to demand variability • Establishing intraday governance, playbooks, and escalation paths to support service level performance • Monitoring daily service, backlog, and productivity performance and proactively mitigating risks • Leading, coaching, and developing teams across short‑term planning, scheduling, and real‑time management functions • Serving as the primary workforce planning point of contact for Provider Call Operations leadership • Providing clear, actionable insights on staffing risks, trade‑offs, and opportunities • Leading workforce planning transformation initiatives, including process standardization and tool enhancement.

• Own and enforce configuration control across hardware, software, and technical data • Define, implement, and continuously improve configuration management (CM) processes, standards, and tools • Develop and maintain Configuration Management Plans (CMPs) at program and enterprise level • Serve as system owner and administrator for the Product Lifecycle Management (PLM) system • Establish and control part numbering schemes, bills of materials (BOMs), item attributes and lifecycle states • Own the Engineering Change Order (ECO) process end-to-end and establish and maintain product baselines (design, build, as-built) • Lead the Configuration Control Board (CCB): Drive decisions, prevent uncontrolled change, and ensure closure of actions • Support and prepare for configuration audits, manufacturing and readiness reviews, and design reviews • Compile and validate End Item Data Packages (EIDPs) for delivered products • Interface across Engineering, Manufacturing, Supply Chain, Quality, and Program Management to maintain a single authoritative data source

Role Description We are looking for a Manager – AI & Machine Learning Engineering to lead a team of ML engineers in developing and deploying high-impact machine learning solutions across core enterprise functions including Sales, Service, Finance, Order Fulfillment, and Supply Chain. This role requires a strong mix of technical leadership, people management, and strategic alignment to guide ML engineers from ideation through production deployment. You’ll play a key role in shaping the AI/ML delivery roadmap, establishing scalable engineering practices, and driving value through predictive models integrated into critical business workflows. Responsibilities - Team Leadership and Talent Development - Manage, coach, and grow a high-performing team of machine learning engineers, promoting a culture of innovation, collaboration, and continuous learning. - Provide technical direction, architectural oversight, and career mentorship. - Define team objectives and success metrics aligned with enterprise priorities. - Program Execution and Delivery - Drive the successful execution of ML use cases such as customer churn prediction, upsell opportunity scoring, demand forecasting, and operational risk detection. - Work closely with data science, data engineering, product, and business stakeholders to define and deliver scalable ML solutions. - Oversee delivery timelines, model development, deployment readiness, and feedback integration. - ML Engineering and MLOps Strategy - Establish best practices in model development, deployment, and monitoring, using tools like MLflow, SageMaker, Azure ML, Airflow, or Kubeflow. - Guide the team in implementing CI/CD for ML pipelines, model versioning, feature stores, and performance monitoring. - Champion a strong foundation in software engineering, code quality, and reusability in ML development. - Functional & Cross-Domain Focus - Align ML efforts with key business domains such as Sales (lead scoring, renewals), Service (case triage), Finance (forecasting), Order Fulfillment (ETA, risk), and Supply Chain (inventory planning, logistics optimization). - Collaborate with business owners to prioritize high-impact ML use cases and ensure adoption and value realization. - Technology & Architecture Oversight - Partner with data platform and infrastructure teams to scale ML solutions using Snowflake, Datarobots, and enterprise cloud platforms (AWS, Azure, GCP). - Ensure ML models integrate seamlessly with business systems such as Salesforce, Oracle Fusion Cloud, and other operational tools. Qualifications - Required: - 8+ years of experience in machine learning, data science, or engineering roles, with 3+ years in a technical leadership or management capacity. - Proven experience building and deploying machine learning solutions in production environments. - Hands-on background with Python, ML frameworks (scikit-learn, PyTorch, TensorFlow), and orchestration tools. - Strong understanding of MLOps practices, model lifecycle management, and pipeline automation. - Experience working with cross-functional stakeholders to deliver ML-powered business solutions. - Preferred: - Experience supporting business functions such as Sales, Finance, or Supply Chain with applied ML. - Familiarity with cloud platforms (AWS, Azure, or GCP) and enterprise data tools (Snowflake, dbt, Matillion). - Exposure to enterprise platforms such as Oracle Fusion Cloud, Salesforce, or ServiceNow. Company Description