Data Science Manager

We need to output HTML fragment with sections: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. Only include sections that have real specific content. We must not repeat job title. Must not include Job ID etc. Must remove boilerplate like EEO, legal, etc. Must preserve all substantive info. We need to parse the posting. Content: - Intro about Unilever, GDT, About The Role (some description). That could go under "About the role" heading. - Key Responsibilities: bullet list under Technical & Product Leadership, People Leadership & Capability Building, Stakeholder & Business Partnership, Ways of Working & Continuous Improvement. We need to put these as list items under Responsibilities. - About You: Experience & Expertise, Leadership & Mindset. Could be Requirements, Qualifications, Skills sections. We need to decide which content goes where. We have sections: Experience & Expertise (degree, proven experience, strong background in data science modelling, etc). That's qualifications/requirements. Leadership & Mindset: strong people leader, communicator, strategic thinker, etc. That's skills/qualities. We also have "What We Offer": leadership role, opportunity, collaborative culture, direct contribution. That's like Benefits? Actually it's more about role benefits. But we also have explicit Pay, Bonus, LTI, Benefits sections later. We need to include Pay: The pay range for this position is $103,000 to $154,400. Also mention bonus eligible, LTI eligible. Benefits: Unilever employees are eligible to participate in our benefits plan... list health insurance, retirement savings, life insurance, disability, parental leave, sick leave, paid vacation and holidays, voluntary benefits. Schedule: Not mentioned. So we should not include Schedule section. We should not include sections with filler like "commensurate with experience". Not present. We need to output only HTML fragment, no /. Use

for section headings. Use

• for any list of duties/requirements/benefits. Use

  for prose. We must not repeat job title as heading. So we will not have a heading for "Data Science Manager". We'll start with About the role. We need to preserve all substantive information. Must not invent or summarize away detail. Must not omit details like "exposure to agent-based AI systems is a strong advantage." Must keep. We need to decide how to split content into sections. Potential sections: - About the role: includes the paragraph about role description (starting from "About The Role As a Data Science Manager..."). - Responsibilities: includes the bullet points under Key Responsibilities (Technical & Product Leadership, People Leadership & Capability Building, Stakeholder & Business Partnership, Ways of Working & Continuous Improvement). We'll need to capture each bullet's content. - Requirements: maybe include Experience & Expertise (degree, proven experience, strong background...). Could also include Leadership & Mindset as Skills? But we have separate Skills section maybe. - Qualifications: maybe degree qualification etc. But we can combine. Given instruction: Prefer a few substantive sections over many thin ones. So we could have About the role, Responsibilities, Requirements (which includes experience, expertise, leadership mindset), Benefits, Pay. Possibly also Skills? But we could incorporate leadership mindset into Requirements. We need to include Pay section with the pay range and bonus/LTI info. Benefits section with benefits details. We must not include Schedule. We must not include boilerplate like EEO, legal, etc. We must not include "What We Offer" as separate section? It could be considered part of Benefits or About the role? But we already have About the role covering role description. The "What We Offer" includes leadership role, opportunity to build teams, collaborative culture, direct contribution. That's more like benefits/role highlights. Could be included under Benefits or maybe a separate section like "What We Offer". But we only have allowed headings: About the role, Responsibilities, Requirements, Qualifications, Skills, Benefits, Pay, Schedule. So we could put the "What We Offer" content under Benefits (since it's about what the role offers). However Benefits already defined as benefits plan. But we could still include those points under Benefits as additional bullet points? Might be okay. But we must not invent sections. We have to use only those headings if we include them. So we can include Benefits and put both the benefits plan and the "What We Offer" points there. However we need to ensure we don't misrepresent. The Benefits section originally is about benefits plan. The "What We Offer" is more about role offerings. But we can combine. Alternatively we could create a separate section like "What We Offer" but not allowed. So we must incorporate into existing allowed headings. Perhaps we can put them under Requirements? No. Under Skills? Not really. Under About the role? Could add after role description. That seems appropriate: About the role can include both description and what the role offers. So we can keep About the role as the initial paragraph plus the "What We Offer" bullet points. Thus:

  About the role

  Data Science Manager Unilever is one of the world’s leading suppliers... (the intro about Unilever, GDT, About The Role). Actually the intro includes Unilever description, GDT, About The Role. We'll keep that as prose. Then after that we can add a

  • for the "What We Offer" points: leadership role shaping high-impact global data science products; opportunity to build and lead teams working on cutting-edge analytics, AI, and cloud technologies; collaborative, inclusive culture that values ownership, learning, and innovation; direct contribution to Unilever’s growth ambitions and sustainability purpose. But note that the "What We Offer" paragraph originally: "What We Offer A leadership role shaping high-impact, global data science products. The opportunity to build and lead teams working on cutting-edge analytics, AI, and cloud technologies. A collaborative, inclusive culture that values ownership, learning, and innovation. Direct contribution to Unilever’s growth ambitions and sustainability purpose." We'll turn each sentence into a list item. Now Responsibilities: we need to capture all bullet points under Key Responsibilities. Let's extract: Technical & Product Leadership: - Own and shape the data science roadmap for products within our Customer Development (CD) portfolio, aligned to commercial priorities and business outcomes. - Lead the design, development, and deployment of diagnostic, predictive, and prescriptive analytics solutions, ensuring robustness, scalability, and interpretability. - Set modelling and analytical standards across the team, covering classical statistical methods, machine learning, and emerging Generative and Agentic AI capabilities. - Ensure solutions are industrialised and production-ready, leveraging cloud platforms (Azure, Databricks) and aligned with MLOps best practices. People Leadership & Capability Building: - Lead, coach, and develop a team of data scientists, supporting both technical growth and career progression. - Foster a culture of high ownership, continuous improvement, and learning, encouraging experimentation while maintaining delivery discipline. - Provide technical guidance and review, acting as a mentor and escalation point for complex analytical challenges. Stakeholder & Business Partnership: - Partner closely with Product, Engineering, and Business stakeholders to shape problem definitions, prioritise initiatives, and drive adoption of data science solutions. - Translate complex analytics into clear, actionable insights for senior stakeholders, supporting data-driven decision-making. - Balance short-term delivery with long-term capability building, ensuring alignment between local market needs and global product strategy. Ways of Working & Continuous Improvement: - Champion agile ways of working, enabling fast-paced experimentation and iterative delivery. - Identify opportunities to build reusable frameworks, scale solutions, and feed innovation into the global CD product pipeline. We'll put each as
  • under a
    inside Responsibilities. Now Requirements/Qualifications/Skills. We have "About You" section with Experience & Expertise and Leadership & Mindset. Experience & Expertise: - Degree qualified in a relevant technical discipline (Data Science, Computer Science, Engineering, Mathematics, Statistics, Econometrics, or similar). - Proven experience leading data science teams working on complex analytics initiatives in a commercial or product-driven environment. - Strong background in data science modelling, with deep expertise in several of the following: * Econometric modelling (Regression, Bayesian approaches) especially in the context of pricing and promotions in the CPG/Retail space * Time series forecasting * Simulation and optimisation tools * Experience designing and industrialising machine learning solutions at scale; exposure to agent-based AI systems is a strong advantage. * Advanced proficiency in Python, Spark, and modern analytics stacks; hands-on experience with Databricks and Azure. Leadership & Mindset: - Strong people leader with experience coaching and developing talent in multidisciplinary teams. - Excellent communicator, able to influence and align senior stakeholders through clear storytelling and data-driven narratives. - Strategic thinker with the ability to prioritise effectively and focus teams on what delivers the most value. - Comfortable operating in a fast-paced, global, and ambiguous environment, balancing delivery with longer-term vision. - High ethical standards in data usage, governance, and decision-making. We need to decide how to split into Requirements, Qualifications, Skills. We could treat Requirements as the experience and expertise (degree, proven experience, background). Qualifications could be the specific technical expertise (econometric modelling, time series, simulation, ML, Python/Spark/Databricks/Azure). Skills could be leadership & mindset (people leader, communicator, strategic thinker, etc). But we must not create thin sections. Perhaps combine Requirements and Qualifications into one section? But we have to follow instruction: use headings only when posting has real specific content. We can include all three if they have content. We need to avoid filler like "commensurate with experience". Not present. Thus we can have:

    Requirements

    Degree qualified in a relevant technical discipline (Data Science, Computer Science, Engineering, Mathematics, Statistics, Econometrics, or similar). Proven experience leading data science teams working on complex analytics initiatives in a commercial or product-driven environment.

    Qualifications

    • Strong background in data science modelling, with deep expertise in several of the following:
    • Econometric modelling (Regression, Bayesian approaches) especially in the context of pricing and promotions in the CPG/Retail space
    • Time series forecasting
    • Simulation and optimisation tools
    • Experience designing and industrialising machine learning solutions at scale; exposure to agent-based AI systems is a strong advantage.
    • Advanced proficiency in Python, Spark, and modern analytics stacks; hands-on experience with Databricks and Azure.
    But nested lists may be okay but we need to keep simple: we could just have a single
    with items, each item containing the subpoints as part of the text. Simpler: each qualification as a
    • with the detailed text. Thus:

      Qualifications

      • Strong background in data science modelling, with deep expertise in econometric modelling (Regression, Bayesian approaches) especially in the context of pricing and promotions in the CPG/Retail space.
      • Strong background in data science modelling, with deep expertise in time series forecasting.
      • Strong background in data science modelling, with deep expertise in simulation and optimisation tools.
      • Experience designing and industrialising machine learning solutions at scale; exposure to agent-based AI systems is a strong advantage.
      • Advanced proficiency in Python, Spark, and modern analytics stacks; hands-on experience with Databricks and Azure.
      But that repeats "Strong background in data science modelling, with deep expertise in". Might be redundant but preserves detail. Could combine: "Strong background in data science modelling, with deep expertise in several of the following: econometric modelling (Regression, Bayesian approaches) especially in the context of pricing and promotions in the CPG/Retail space; time series forecasting; simulation and optimisation tools; experience designing and industrialising machine learning solutions at scale (exposure to agent-based AI systems