Data Science Manager

Unilever•Minneapolis, MN

1d•$103,000 - $154,400

About The Position

Unilever is a leading global supplier of Food, Home, and Personal Care products. The Data Foundation within Global Data & Technology (GDT) aims to make Unilever data-intelligent, empowering business decisions through data, analytics, and AI. This role is within the Customer Development XOPS team, focusing on leading data science delivery, shaping technical direction, and developing high-performing teams. It's a hands-on leadership position requiring technical depth, people management, and business partnership. The manager will own the end-to-end data science lifecycle, translating business problems into scalable analytics solutions and ensuring their integration into decision-making processes.

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.
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.
Advanced proficiency in Python, Spark, and modern analytics stacks.
Hands-on experience with Databricks and Azure.
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.

Nice To Haves

Exposure to agent-based AI systems is a strong advantage.

Responsibilities

Own and shape the data science roadmap for products within the 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.
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.
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.
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.