Senior Manager, Data Science

Electrolux Group•Charlotte, NC

3d•Onsite

About The Position

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. Based at the company’s Corporate North American headquarters in Charlotte, NC, the role reports to the Vice President of Commercial, North America. The Senior Manager 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. The role shapes a flexible portfolio of opportunities, allocating resources to business questions with the greatest potential for profitable growth, consumer impact, and operational improvement. Success is measured by adoption and realized outcomes, rather than the number of models produced. The leader combines data science expertise, product-oriented leadership, and business judgment to translate complex analysis into action and take solutions from idea through deployment, adoption, measurement, and continuous improvement. The Senior Manager will guide data science products through problem framing, data assessment, experimentation, modeling, validation, deployment, monitoring, and improvement. They will partner with business and technical teams to build reliable data pipelines and production pathways, establish responsible and fit-for-purpose practices, and foster adoption of solutions. They will also build, coach, and develop a high-performing data science pod, establish team practices and technical standards, and develop reusable capabilities that can scale across categories, channels, and functions.

Requirements

Bachelor’s degree in Data Science, Statistics, Computer Science, Engineering, Economics, Operations Research, Applied Mathematics, or a related quantitative field.
At least 6 years of progressive experience in data science, machine learning, advanced analytics, or a closely related field, including delivery of production solutions tied to business outcomes.
At least 3 years of people leadership experience, with demonstrated success building, coaching, and developing technical teams.
Strong command of statistical modeling, machine learning, experimental design, causal inference, forecasting, and model evaluation.
Fluency in modern analytical programming and data environments.
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 simplifying complex topics, influencing without direct authority, and making clear recommendations.
Product mindset and experience moving analytical solutions from concept to scalable deployment and sustained adoption.
Experience with 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.

Nice To Haves

Advanced degree in a quantitative field.

Responsibilities

Partner with senior leaders to identify and size high-value opportunities across Commercial, Product Line, D2C, Sales, consumer lifecycle, pricing, promotion, assortment, demand, and related priorities.
Own a dynamic portfolio of data science initiatives, balancing near-term results with longer-term capability building and reallocating effort as business priorities change.
Create 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, and communicate progress and recommendations to senior leadership.
Translate ambiguous business questions into focused analytical products and decisions.
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.
Partner with IT, Data Engineering, Digital Product, Analytics, and platform teams on 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 clear narratives, transparent recommendations, user-centered design, training, and ongoing engagement with business owners.
Set standards for model quality, reproducibility, documentation, monitoring, privacy, security, and governance.
Champion responsible use of analytics and AI, including human oversight, explainability, bias assessment, and compliance with company policies.
Evaluate emerging methods and tools and adopt technology where it improves speed, quality, scalability, or business value.
Promote data literacy and help leaders understand the potential and limitations of data science.
Build, lead, coach, and develop a data science pod and foster a culture of curiosity, accountability, practical innovation, and measurable impact.
Define team roles, operating rhythms, technical standards, and development plans; attract and retain data science talent.
Remain technically engaged to challenge methods, guide solution design, remove barriers, and improve the quality of the team’s work.
Build reusable capabilities, methods, and assets so successful solutions can scale across categories, channels, and functions.