Auxiliar Jurídico

Urquieta & Hernández Despacho Legal; San Nicolás de los Garza; À distance; MXN 600 000 - 1 000 000; Plein temps

Résumé du poste

Urquieta & Hernández Despacho Legal seeks a Process Optimization Consultant to join a remote-first, globally distributed team serving Monterrey, Nuevo León, MX and beyond. You will identify inefficiencies, map processes, and implement streamlined solutions to improve client operational performance from anywhere.

The ideal candidate has a bachelor's degree in Industrial Engineering, Operations Management, or Business Administration and 3–5 years of experience in process improvement, with Lean,

Qualifications

• Bachelor's degree in a related field (Industrial Engineering, Operations Management, Business Administration)
• 3–5 years in process improvement, operational consulting, or similar
• Experience with Lean, Six Sigma, BPMN or related methodologies
• Strong analytical and problem-solving skills with data interpretation ability

Responsabilités

• Analyze existing business processes to identify bottlenecks and waste.
• Map current state processes and design future state processes with best practices.
• Develop and implement strategies for process re-engineering and automation.
• Utilize Lean, Six Sigma, BPMN or other CI methodologies to drive efficiency gains.
• Collaborate with cross-functional teams to gather process data and insights.
• Create detailed documentation of processes, policies and procedures.
• Implement performance metrics to monitor process effectiveness and track improvements.
• Train employees on new processes and facilitate change management initiatives.
• Provide recommendations for technology adoption to support optimization goals.

Connaissances

Lean

Six Sigma

BPMN

Data analysis

Formation

Bachelor's degree in Industrial Engineering

Bachelor's degree in Operations Management

Bachelor's degree in Business Administration

Description du poste

About the Role

Our client, a renowned management consulting firm, is seeking a dedicated Process Optimization Consultant to join their globally distributed, remote-first team. Based remotely but serving clients in the Monterrey, Nuevo León, MX region and beyond, you will focus on identifying inefficiencies and implementing streamlined solutions across various business functions. Your expertise will be critical in helping organizations enhance their operational performance, reduce costs, and improve overall productivity. This role offers the unique advantage of working from anywhere, while contributing to significant operational improvements for our clients. We are looking for a proactive individual with a keen eye for detail and a passion for driving operational excellence through systematic analysis and strategic implementation. The ideal candidate thrives in a remote setting and excels at collaborating virtually.

Key Responsibilities

• Analyze existing business processes to identify bottlenecks, waste, and areas for improvement.
• Map current state processes and design future state processes incorporating best practices.
• Develop and implement strategies for process re-engineering and automation.
• Utilize Lean, Six Sigma, or other continuous improvement methodologies to drive efficiency gains.
• Collaborate with cross-functional teams to gather process data and insights.
• Create detailed documentation of processes, policies, and procedures.
• Implement performance metrics to monitor process effectiveness and track improvements.
• Train employees on new processes and facilitate change management initiatives.
• Provide recommendations for technology adoption to support process optimization goals.

Requirements

• Bachelor's degree in Industrial Engineering, Operations Management, Business Administration, or a related field.
• Minimum of 3-5 years of experience in process improvement, operational consulting, or a similar role.
• Demonstrated experience with Lean, Six Sigma, BPMN, or other process improvement methodologies.
• Strong analytical and problem-solving skills, with the ability to interpret complex data sets