Senior Manager, Data Science

Job Description

Join us to create change and have an impact in homes around the world.

At Electrolux Group, our vision is to be the home appliance industry leader in consumer satisfaction by delivering outstanding lifetime experiences with solutions that always get better. We share ideas and collaborate so that together, we can develop solutions that deliver more enjoyable and sustainable living.

Come, join us as you are. We believe diverse perspectives make us stronger and more innovative. In our global community of people from 100+ countries, we listen to each other, actively contribute and grow together.

Join us in our exciting quest to build the future home!

Where you’ll be:

This position will be based at our Corporate North American headquarters in Charlotte, NC.

About the Role:

Electrolux is seeking a strategic, entrepreneurial, and technically credible leader to build and lead a high-impact Data Science pod for Business Area North America. Reporting to the Vice President of Commercial, North America, this role will identify, prioritize, and deliver advanced analytics and artificial intelligence solutions that create measurable business value across Commercial, Product Line, Direct-to-Consumer, Sales, and other priority areas.

This is not a narrowly defined analytics role. The Senior Manager will shape a flexible portfolio of opportunities, moving resources toward the questions with the greatest potential for profitable growth, consumer impact, and operational improvement. Success will be measured by adoption and realized outcomes, not by the number of models produced.

The ideal candidate combines data science depth, product-oriented leadership, strong business judgment, and the ability to translate complex analysis into action. They will build the pod, establish a disciplined value-delivery model, and partner across a matrixed organization to take solutions from idea through deployment, adoption, measurement, and continuous improvement.

Key Responsibilities:

Business Value & Portfolio Strategy

- Partner with senior leaders to identify and size high-value opportunities across Commercial, Product Line, D2C, Sales, consumer lifecycle, pricing, promotion, assortment, demand, and adjacent business priorities.
- Own a dynamic portfolio of data science initiatives, balancing near-term wins with longer-term capability building and reallocating effort as business priorities evolve.
- Create clear business cases, success measures, and value hypotheses before work begins; use stage gates to stop, reshape, or accelerate initiatives based on evidence.
- Track realized value with Finance and business owners, including revenue, margin, cost, productivity, consumer, and adoption outcomes; communicate progress and recommendations to senior leadership.
- Act as an internal thought partner who turns ambiguous business questions into focused analytical products and decisions.

Solution Development, Deployment & Adoption

- Guide the end-to-end lifecycle of data science products, including problem framing, data assessment, experimentation, modeling, validation, deployment, monitoring, and improvement.
- Prioritize solutions that can be embedded into business processes, customer experiences, and frontline decision-making rather than one-time analyses.
- Partner with IT, Data Engineering, Digital Product, Analytics, and platform teams to establish reliable data pipelines, production pathways, and fit-for-purpose architecture.
- Lead test design and measurement, including pilots, controlled experiments, incrementality assessment, forecasting, and post-launch performance reviews.
- Drive adoption through simple narratives, transparent recommendations, user-centered design, training, and sustained engagement with business owners.

Data, AI & Responsible Practice

- Set fit-for-purpose standards for model quality, reproducibility, documentation, monitoring, privacy, security, and governance.
- Champion responsible use of advanced analytics and AI, including appropriate human oversight, explainability, bias assessment, and compliance with company policies.
- Evaluate emerging methods and tools pragmatically, adopting technology where it improves speed, quality, scalability, or business value.
- Promote strong data literacy and help leaders understand both the potential and limitations of data science.

Pod Leadership & Capability Building

- Build, lead, coach, and develop a pod of data scientists, creating a high-performance culture grounded in curiosity, accountability, practical innovation, and measurable impact.
- Define team roles, operating rhythms, technical standards, and development plans; attract and retain strong data science talent.
- Remain technically engaged enough to challenge methods, guide solution design, remove barriers, and raise the quality of the team’s work.
- Build reusable capabilities, methods, and assets that allow successful solutions to scale across categories, channels, and functions.

Minimum Qualifications:

- Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, Economics, Operations Research, Applied Mathematics, or a related quantitative field; advanced degree preferred.
- 6+ years of progressive experience in data science, machine learning, advanced analytics, or a closely related field, including experience delivering production solutions tied to business outcomes.
- 3+ years of people leadership experience, with demonstrated success building, coaching, and developing technical teams.

Qualifications:

- Strong command of statistical modeling, machine learning, experimental design, causal inference, forecasting, and model evaluation; fluency in modern analytical programming and data environments.
- Proven ability to translate ambiguous business needs into a prioritized roadmap, clear technical work, and measurable financial or operational impact.
- Experience partnering with business, product, technology, data engineering, finance, legal, privacy, and governance stakeholders in a matrixed organization.
- Executive-level communication skills, including the ability to simplify complex topics, influence without direct authority, and make clear recommendations.
- Demonstrated product mindset and experience moving analytical solutions from concept to scalable deployment and sustained adoption.
- Experience in consumer durables, retail, e-commerce, CPG, manufacturing, or another complex omnichannel environment.
- Experience with consumer, transaction, digital behavior, point-of-sale, pricing, promotion, product, or supply chain data.
- Experience establishing value measurement, model governance, MLOps, or responsible AI practices.

Please be advised that we are unable to offer visa sponsorship for this position at this time.

Find more on:

Electrolux Group North America:

https://www.linkedin.com/company/electrolux/life/northamerica/ Electrolux Group Careers: https://career.electroluxgroup.com/global/en

Electrolux Consumer Products, Inc. is an Equal Opportunity Employer (EOE). Qualified applicants are considered for employment without regard to age, race, color, religion, sex, national origin, sexual orientation, disability, or veteran status. If you need assistance or an accommodation during the application process because of a disability, it is available upon request through [email protected]. The company is pleased to provide such assistance, and no applicant will be penalized as a result of such a request.