Data Science Manager

Unilever

Data Science Manager at Unilever responsible for data science delivery and team leadership in the Customer Development XOPS team. Shaping analytics solutions aligned to commercial priorities and business outcomes.

Posted 7/21/2026full-timeHoboken • 🇺🇸 United StatesMid-LevelSenior💰 $103,000 - $154,400 per yearWebsite

Core Competencies

Role fit

Core Competencies

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Demonstrates expertise in leading data science teams and developing robust analytics solutions, with a strong focus on machine learning, econometric modeling, and cloud platforms. Capable of translating complex data insights into actionable strategies while fostering a culture of continuous improvement and agile methodologies.

Highest-signal resume keywords

Data Science Team LeadershipMachine Learning Solution DesignEconometric Modeling ExpertiseAdvanced Python ProficiencyCloud Platform Experience (Azure, Databricks)

ATS Keywords

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Applicant Tracking System Keywords

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Hard Skills

Data Science ModelingEconometric ModelingTime Series ForecastingSimulation and Optimization ToolsPredictive AnalyticsPrescriptive AnalyticsStatistical MethodsGenerative AIAgentic AIMLOps Best Practices

Soft Skills

Coaching and MentoringCollaborationProblem-SolvingCommunicationContinuous Improvement

Tools & Technologies

AzureDatabricksSparkModern Analytics Stacks

Industry Keywords

Customer DevelopmentCommercial PrioritiesData-Driven Decision-MakingAgile MethodologiesCPG/Retail

Tech Stack

Tools & technologies

AzureCloudPythonSpark

About the role

Key responsibilities & impact

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.
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.
Champion agile ways of working, enabling fast-paced experimentation and iterative delivery.

Requirements

What you’ll need

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:
o Econometric modelling (Regression, Bayesian approaches) especially in the context of pricing and promotions in the CPG/Retail space
o Time series forecasting
o 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.

Benefits

Comp & perks

Health insurance (including prescription drug, dental, and vision coverage)
Retirement savings benefits
Life insurance and disability benefits
Parental leave
Sick leave
Paid vacation and holidays
Access to numerous voluntary benefits