Senior Data Scientist

Brambles•Orlando, FL

13d•Hybrid

About The Position

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 13,000 people and operate in 60 countries. Through our pioneering and sustainable share-and-reuse business model, the world’s biggest brands trust us to help them transport their goods more efficiently, safely and with less environmental impact. What does that mean for you? You’ll join an international organization big enough to take you anywhere, and small enough to get you there sooner. You’ll help change how goods get to market and contribute to global sustainability. You’ll 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 our Hybrid Work Model. POSITION PURPOSE Develop and apply advanced data-driven solutions using machine learning and statistical models to address complex business challenges. Build trust and drive the adoption of data science solutions by communicating insights and technical solutions to stakeholders in a clear and actionable way. Shapes project collaborations across teams, providing mentorship, and driving capability development across the Data Science team

Requirements

Technology related degree and/or a minimum of 3 years working experience in a similar role.
Previous data science experience applying advanced algorithms to address practical problems.
Advanced hands-on experience in Python data libraries.
Proven track record in developing machine learning models (time series, clustering, regression analysis, deep learning).
SQL experience (Ability to read/write complex SQL queries).
Worked with measures and KPIs.
Working on remote teams.
Proved experience with Project Management (manage priorities, standardization, automation, troubleshooting and long-term planning).
Experience with process automation and workload reduction.
Experience implementing data new solutions from scratch.
Experience with software development (applied to data science).
Proficient using Microsoft tools: Excel, Access, Word, Power Point, Visio.
Coding tools and Machine Learning.
Data management and statistical analysis.
Business mindset.
Strong analytic skills.
Customer oriented.
Strong communication skills.
Flexible and able to work under pressure.
Diplomatic and sensitive when serving customers of a different mentality and culture.
Ability to work autonomously.
Ability to work with remote teams.
Adaptability
Bitbucket
Cloud Infrastructure (Aws)
Code Reviews
Databricks Platform
Data Science
Data Storytelling
Empathy
Experimentation
Git
Machine Learning (ML)
Python (Programming Language)
SQL Tools
Taking Ownership
Teamwork
Understand Customers

Nice To Haves

Experience with NLP and computer vision is a bonus.

Responsibilities

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.

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