Senior Manager, Data Science

Electrolux•Charlotte, NC

1d•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 will shape a flexible portfolio of opportunities, directing resources toward those 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 role combines data science depth, product-oriented leadership, business judgment, and the ability to translate complex analysis into action. The Senior Manager 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.

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, including 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 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.

Nice To Haves

Advanced degree in a quantitative field is preferred.

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 adjacent priorities.
Own a dynamic portfolio of data science initiatives, balancing near-term results with longer-term capability building and reallocating effort as priorities evolve.
Create business cases, success measures, and value hypotheses before work begins, and 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.
Turn 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 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 clear narratives, transparent recommendations, user-centered design, training, and sustained engagement with business owners.
Set fit-for-purpose standards for model quality, reproducibility, documentation, monitoring, privacy, security, and governance.
Champion responsible analytics and AI practices, including human oversight, explainability, bias assessment, and compliance with company policies.
Evaluate emerging methods and tools, adopting 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, fostering 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 that allow successful solutions to scale across categories, channels, and functions.