CHEP helps move more goods to more people in more places than any other organization on earth via our 347 million pallets crates and containers. We employ approximately 13000 people and operate in 60 countries. Through our pioneering and sustainable share-and-reuse business model the worlds biggest brands trust us to help them transport their goods more efficiently safely and with less environmental impact.

What does that mean for you Youll join an international organization big enough to take you anywhere and small enough to get you there sooner. Youll help change how goods get to market and contribute to global sustainability. Youll be empowered to bring your authentic self to work and be surrounded by diverse and driven professionals. And you can maximize your work-life balance and flexibility through ourHybrid Work Model.

Job Description

Key Responsibilities May Include:

• Collaborate with key stakeholders to identify business challenges translating ambiguous problems into structured analyses using statistical modelling and machine learning algorithms.
• Lead the selection validation and optimization of models to discover meaningful patterns and insights ensuring models remain relevant reliable and scalable.
• Drive continuous integration and deployment of data science solutions optimizing performance through advanced machine learning techniques code reviews and best practices.
• Develop and deliver sophisticated visualizations dashboards and reports translate complex data into clear actionable insights for business stakeholders.
• Present technical solutions to business stakeholders using creative methods to explain complex concepts increase understanding and encourage solution adoption.
• Mentor and develop junior data scientists fostering a culture of continuous learning knowledge sharing and skills development within the organization.
• Write clean high-quality code ensuring all outputs pass quality assurance checks and contribute to the development of novel solutions to solve complex business problems.
• Stay informed on industry trends emerging tools and techniques applying them to improve data science practices and encourage innovation within the team.
• Lead strategy development for one or more data products managing roadmaps identifying requirements and collaborating with business stakeholders to ensure alignment with business goals.

POSITION PURPOSE

We are seeking a Senior Machine Learning Engineer to design build deploy and operate scalable machine learning and AI solutions in production. This role sits at the intersection of MLOps traditional data science modeling and software engineering with opportunities to work on AI/GenAI engineering use cases.

You will work closely with Data Scientists and Engineers to productionize ML and emerging GenAI solutions owning the full lifecycle from model development through deployment monitoring and iteration.

SCOPE

Machine Learning models for Advanced D&A Americas.

Data products initiatives for Advanced D&A Americas.

GenAI initiatives for Advanced D&A Americas.

MAJOR / KEY ACCOUNTABILITIES

Build maintain and optimize end to end ML pipelines covering data ingestion feature engineering training evaluation deployment inference and monitoring using Databricks and related tooling.

Collaborate closely with Data Scientists to translate experimental and research grade models into reliable scalable and secure production services that meet business and technical requirements.

Apply MLOps best practices including model versioning experiment tracking monitoring and automated deployments.

Develop and deploy traditional ML models (e.g. regression classification forecasting NLP) to solve business problems.

Implement runtime monitoring dashboards and alerting mechanisms to detect performance degradation data anomalies and system failures in near real time.

Support AI / GenAI initiatives including LLM based prototypes and production workflows where applicable.

Collaborate with product owners data scientists engineers and business stakeholders to define model requirements SLAs success metrics and deployment constraints.

Integrate ML solutions into downstream systems via APIs batch pipelines or event driven processes.

Write high quality maintainable code following engineering best practices with version control and CI/CD in Bitbucket.

Troubleshoot and optimize model performance scalability latency and cost in production environments.

Provide guidance and best practices to data scientists and engineers on production ready ML development and MLOps workflows.

Evaluate emerging tools frameworks and practices to enhance the organizations ML and GenAI operational maturity.

MEASURES

ML models are reliable scalable and observable in production environments

Reduced time and friction moving from experimentation to production ML systems

High availability and reliability of ML pipelines and inference services

Strong collaboration with Data and cross functional teams resulting in business impacting ML solutions

Clear observability into model performance data quality and system health

Adoption of standardized patterns for ML development and deployment across the team

KEY CONTACTS

Internal: Data & Analytics Americas Processes Digitalization Supply Chain Commercial Serialization Finance Digital

QUALIFICATIONS

Bachelors or masters degree in computer science Engineering Data Science Mathematics or a related field or 3 years of equivalent professional experience in a related role

Strong foundation in machine learning algorithms and applied modeling techniques

Demonstrated ability to build and operate production grade software systems is a plus

Proven ability to work in ambiguous problem spaces and evolving AI landscapes

EXPERIENCE

3 years of experience in Machine Learning Engineering Applied Machine Learning or a closely related role

Hands on experience deploying and supporting ML models in production

Proven experience using ML lifecycle management tools such as MLflow (preferred) or similar platforms

Experience using Databricks or similar platforms for data processing and ML workloads

Proven collaboration with Data Scientists and Engineers in cross functional teams

Experience supporting both early stage experimentation and production systems

SKILLS AND KNOWLEDGE

Strong understanding of supervised and unsupervised learning techniques

Feature engineering model evaluation and performance optimization

Experience operationalizing models beyond notebooks

Building and maintaining ML pipelines (training inference retraining)

Model versioning experiment tracking and reproducibility

Monitoring for model performance data drift and pipeline failures

CI/CD practices for ML workflows

Strong proficiency in Python

Writing testable maintainable production quality code

Git based version control workflows

Experience integrating ML into applications or services

Exposure to LLMs embeddings prompt engineering or retrieval augmented generation (RAG)

Experience moving GenAI use cases from prototype to production

Familiarity with evaluating GenAI outputs and monitoring cost latency and quality

Experience building or consuming REST APIs for model inference

Understanding of distributed systems and data pipelines

Remote Type

Skills to succeed in the role

We are an Equal Opportunity Employer and we are committed to developing a diverse workforce in which everyone is treated fairly with respect and has the opportunity to contribute to business success while realizing his or her potential. This means harnessing the unique skills and experience that each individual brings and we do not discriminate against any employee or applicant for employment because of race color sex age national origin religion sexual orientation gender identity status as a veteran and basis of disability or any other federal state or local protected class.

Individuals fraudulently misrepresenting themselves as Brambles or CHEP representatives have scheduled interviews and offered fraudulent employment opportunities with the intent to commit identity theft or solicit money. Brambles and CHEP never conduct interviews via online chat or request money as a term of employment. If you have a question as to the legitimacy of an interview or job offer please contact us at