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Data Science Lead

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Brambles Group

Jan 8

  • Job
    Full-time
    Senior Level
  • Data
    IT & Cybersecurity
  • Madrid
  • Quick Apply

AI generated summary

  • You need 7+ years in leadership, data science expertise, experience with ML tools, strong communication skills, team engagement, and proficiency in AWS, Databricks, and Agile frameworks.
  • You will lead a data science team, ensure quality standards, provide mentorship, drive innovation, manage performance, and align analytics initiatives with business objectives while fostering collaboration.

Requirements

  • 7+ years’ leadership experience leading, managing or influencing multiple functions, geographies and stakeholders (ideally in a Global Digital Program). Experience managing a large, multi-cultural, diverse team.
  • Significant previous experience creating and leading cross functional and multi-country business change programmes.
  • Familiarity with modern data science tools and technologies such as continuous integration, build, deliver, test-driven development and automated acceptance testing.
  • Experience of managing a portfolio of programmes and initiatives within a matrix structure, creating/delivering customer value propositions, and leading technology related programmes.
  • Communicating and inspiring confidence at a senior level with technical and non-technical audiences, with the ability to shape strong presentations and narratives that influence and commit people to change.
  • Experience in multi-facility, international organisations with diverse multi-cultural, corporate cultures.
  • Strong team engagement and motivation skills. You’ll be adaptable and able to pivot in a dynamic environment, a digital enthusiast, a coach and a communicator.
  • Ability to lead in a matrix and build capabilities in global teams.
  • An advocate and ambassador of the ‘test, learn, build or pivot’ approach.
  • Excellent understanding of machine learning techniques and algorithms.
  • Knowledge of implementing Data Science, Machine Learning and GenAI solutions at scale.
  • Experience with common statistical techniques and data science toolkits.
  • Strong quantitative and analytical skills and experience with data visualisation tools.
  • Applied statistics skills, such as distributions, statistical testing, regression etc.
  • Experience with AWS, Databricks and Dataiku.
  • Strong understanding of data structures and algorithms plus solution and technical design.
  • Able to quickly pick up new programming languages, technologies, and frameworks.
  • Strong knowledge of applied data science.
  • Significant expertise in machine learning algorithms and data science methods.
  • Strong data wrangling experience with structured and unstructured data.
  • Experience with various programming and scripting languages, databases, processing and storage frameworks plus coverage with various hyperparameter tuning approaches.
  • Understanding and experience with CRISP-DM and Agile data science framework.
  • Ability to identify and resolve both people and process related issues.
  • Bachelor’s Degree in Computer Science, Statistics or equivalent Technical Degrees.
  • Agile (SCRUM) Certification.
  • Development Coach Certification.

Responsibilities

  • Foster Team Excellence: Lead and manage a multidisciplinary team of data scientists, machine learning engineers, and GenAI developers, ensuring they are empowered to deliver high-quality, scalable solutions.
  • Quality Assurance: Establish and enforce best practices for coding, model development, and deployment processes to ensure all outputs meet organisational standards.
  • Performance Management: Conduct regular 1:1s, performance reviews, and provide constructive feedback to support individual and team growth.
  • Collaboration: Act as a bridge between squads and stakeholders, ensuring alignment and collaboration across teams to maximise impact.
  • Upskilling: Develop and execute training plans to enhance the team’s technical capabilities.
  • Mentorship: Provide technical mentorship and career guidance to team members, fostering a culture of continuous learning and professional development.
  • Knowledge Sharing: Encourage and facilitate the sharing of knowledge, tools, and techniques within the team and across the organisation.
  • Strategic Alignment: Contribute to the development and execution of the organisation’s advanced analytics strategy, ensuring alignment with business objectives.
  • Innovation: Identify and drive opportunities for applying advanced analytics, machine learning, and Generative AI to solve complex business challenges.
  • Roadmap Development: Collaborate with stakeholders to define and prioritise the roadmap for analytics initiatives, balancing innovation with delivery timelines.
  • Emerging Trends: Stay ahead of industry trends and emerging technologies, assessing their relevance and potential impact on the organisation.
  • Scalability: Ensure the team’s processes, tools, and solutions are scalable and can support organisational growth.
  • Data Governance: Champion robust data governance practices, ensuring compliance with legal, ethical, and organisational standards in all analytics initiatives.
  • Metrics and Impact: Establish and monitor KPIs to measure the team’s performance and the business impact of advanced analytics solutions.

FAQs

What is the main responsibility of the Data Science Lead?

The Data Science Lead is responsible for managing, developing, and inspiring a high-performing team of data scientists, machine learning engineers, and Generative AI developers, with a focus on fostering technical excellence and ensuring quality, scalability, and integrity of outputs.

How many years of experience is required for this role?

A minimum of 7 years of leadership experience is required, ideally in managing large, multicultural, and diverse teams.

Is there any specific educational qualification needed for this position?

Yes, a Bachelor’s Degree in Computer Science, Statistics, or an equivalent technical degree is essential.

What type of working environment does CHEP provide?

CHEP operates on a Hybrid Work Model, allowing for a combination of remote and in-office work to maximize work-life balance and flexibility.

What are some key skills required for this role?

Key skills include strong team engagement and motivation, excellent communication and presentation skills, a deep understanding of machine learning techniques, data wrangling experience, and experience with various programming languages and data science tools.

Are there opportunities for professional growth within this role?

Yes, the Data Science Lead will support professional growth by providing mentorship, developing training plans, and fostering a culture of continuous learning within the team.

What analytics frameworks or methodologies should candidates be familiar with?

Candidates should have an understanding and experience with CRISP-DM and Agile data science frameworks.

Is certification in Agile or development coaching advantageous for applicants?

Yes, having an Agile (SCRUM) Certification or Development Coach Certification is considered desirable for applicants.

Will the Data Science Lead work independently or collaborate with others?

The Data Science Lead will collaborate closely with the Head of Global Data & Analytics and the Head of Data Science, as well as act as a bridge between squads and stakeholders.

What is the primary focus of the Data Science Lead in terms of team management?

The primary focus is on fostering team excellence, ensuring quality assurance, conducting performance management, and encouraging knowledge sharing within the team.

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Brambles Group

Transportation
Industry
10,001+
Employees
1875
Founded Year

Mission & Purpose

Brambles is a global supply chain solutions company specialising in pallet and container pooling services through its CHEP and IFCO brands. The company works across various sectors, including fast-moving consumer goods, fresh produce, retail, and manufacturing, providing efficient and sustainable logistics solutions. Brambles' ultimate mission is to connect people with life's essentials through a smarter, more sustainable supply chain. Their purpose is to reduce waste and inefficiencies in global supply chains by promoting circular economy practices, helping to protect the environment and ensure long-term resource sustainability