Operations Supervisor I (Analytics, Data, and DOI)

The Operations Supervisor I is responsible for the day-to-day operational oversight, delivery governance, and people management of multiple technical teams supporting analytics, data engineering, AI (Development, Agentic AI), MIS, and quality control functions. The role ensures consistent execution, quality, capacity management, and stakeholder alignment across teams with diverse skill sets and deliverables.
This position acts as the bridge between individual contributors and functional leadership, translating delivery expectations into operational plans while coaching teams toward consistent performance and continuous improvement.

Operational Management

• Oversee daily operations across multiple teams handling data analysis, reporting, quality control, business process analysis, and AI initiatives.

• Ensure adherence to delivery timelines, SLAs, and quality standards for recurring production outputs and ad hoc requests.

• Monitor workload distribution, capacity utilization, and pipeline health across teams.

• Escalate delivery risks, data quality issues, or resourcing constraints with clear mitigation plans.

Delivery & Quality Governance

• Enforce quality control standards for incoming and outgoing test files, production outputs, and reporting artifacts.

• Drive consistency in MIS, reporting cadence, and metrics definitions across teams.

• Ensure process documentation, handoffs, and audit trails are maintained and followed.

• Partner with leads and senior contributors to improve process efficiency, data management, and AI opportunities identified by the teams.

People Leadership

• Provide day-to-day supervision, coaching, and performance management for analysts, specialists, and engineers.

• Set clear expectations on role responsibilities, priorities, and deliverables.

• Support onboarding, skill development, and role progression in collaboration with functional leads.

• Foster a culture of accountability, continuous improvement, and collaboration across teams.

Stakeholder & Cross‑Functional Collaboration

• Act as primary operational point of contact for internal stakeholders, consultants, and partner teams.

• Translate business requirements into actionable work plans for technical teams.

• Align team execution with broader consulting, analytics, and product delivery objectives.

• Support leadership with status reporting, operational insights, and issue analysis.

Digital Excellence and Optimization

• Identify patterns impacting efficiency, quality, or delivery predictability.

• Promote standardization of workflows, reporting templates, and operational practices where applicable.

• Support implementation of new tools, reporting methods, and governance processes introduced by leadership.

• Oversee AI deployment and integration (Agentic, AI Models, off-the-shelf), ensuring alignment with business goals and collaboration across teams to drive effective implementation and ongoing optimization.

Required Qualifications

• Bachelor’s degree in Business, Statistics, Mathematics, Finance, Data Science, Computer Science, Computer Engineering, or a related field.

• 3–5 years of relevant industry experience in analytics, data operations, AI, or consulting environments.

• Prior experience leading or supervising technical or analytical teams.

• Understanding of:

  • Data analysis and reporting workflows

  • Quality control and data hygiene practices

  • Business process analysis and operational metrics

• Proficient in Microsoft Office, especially Excel; familiarity with Power BI, SQL, or similar tools is an advantage.

• Strong communication skills with the ability to engage both technical and non‑technical stakeholders.

• Demonstrated ability to manage multiple priorities and deliver under tight timelines.

• Comfortable operating in a matrixed environment with multiple stakeholders.

• Detail-oriented with a strong bias toward execution and follow‑through.

• Exposure to AI (Agentic, AI Models, off-the-shelf), workflow optimization, or analytics consulting environments.

• Experience supporting teams with mixed skill profiles (analysts, engineers, specialists).

• Ability to quickly learn and apply enterprise AI tools and technologies to support technical workflows and business objectives

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