Overview:
At Zebra, we are a community of innovators who come together to create new ways of working. United by curiosity and a culture of caring, we develop smart solutions that anticipate our customer’s and partner’s needs and solve their challenges.
Being part of Zebra Nation means you are seen, heard, valued, and respected. Drawing from our unique perspectives, we collaborate to deliver on our purpose. Here you are part of a team pushing boundaries today to redefine the work of tomorrow for organizations, their employees, and those they serve.
You’ll have opportunities to learn and lead in a forward-thinking environment, defining your path to a fulfilling career while channeling your skills toward causes you care about—locally and globally.
Come make an impact every day at Zebra.
What We're Looking For:
Design, optimize, and maintain scalable ETL pipelines using PySpark, and Databricks on cloud platforms (Azure/GCP).
Develop automated data validation process to proactively perform data quality checks.
Employ key Databricks modules (DeltaLake, Unity Catalog, MLFlow) to facilitate creating and scheduling jobs on Databricks.
Optimize the allocation of cloud resources to manage and control cloud cost.
Employ GitHub code management repositories and ensure that best practices are being implemented.
Build and tune ML/AI and optimization models, identify improvement opportunities, and perform experiments to demonstrate incremental value.
Have frequent conversations with Business Stakeholders to understand their requirements and concerns. Explain data deficiencies, model performance/root cause analysis, and model explainability.
Follow best practices in Data Architecture, Coding, and Project Management operations.
Collaborate with cross-functional teams, such as Customers’ Stakeholders, Engagement Managers, Data Ops/Job Monitoring, Product Management & Software Engineering.
Expand the use of analytics, ML/AI, mathematical optimization, Gen-AI/LLMs and Agentic-AI in the context of Retail/CPG business use cases such as anomaly detection, demand forecasting, price elasticity modeling, promotions features & strategy simulation, product cannibalization and halo modeling, markdown optimization, product allocation, reorder/replenishment, size and pack optimization, workforce scheduling & task optimization.
Responsibilities
Master’s degree in engineering, computer science, data science, operations research, statistics, mathematics, quantitative sciences, or relevant work experience.
3 + years of experience in Data Science or Data Engineering with emphasis on the full lifecycle of demand forecasting or time-series modeling projects — including production deployment, monitoring, and ongoing model performance management.
Within that timeframe, experience is expected in: Python/ PySpark , SQL, and relational or NoSQL databases, and cloud resource management.
Key skills and competencies
Hands-on experience designing and operating production demand forecasting systems using statistical time-series methods (ARIMA, exponential smoothing) and regression-based approaches (log-linear regression, LASSO) for price elasticity and promotional lift modeling. Familiarity with hierarchical and weekly SKU-level forecasting at retail scale.
Direct experience in retail or CPG demand forecasting, promotional planning, or supply-chain analytics — either as a vendor delivering forecasting platforms to retailers, or in-house on a retail/CPG demand planning team. Comfort interacting with merchant , replenishment, or supply-chain stakeholders to explain forecasting decisions.
Track record across the full lifecycle of forecasting platforms — designing and building new components for incoming customers, evolving existing pipelines as customer needs change, diagnosing production issues, and explaining model behavior to customer planners and merchants.
Proven experience building end-to-end production-grade data and ML pipelines using PySpark , Python (Pandas/NumPy), and/or SQL — including ingestion of customer transactional and master data, feature engineering for promotional and seasonal effects, model training and scoring at scale, and producing forecasts consumed by downstream merchandising and replenishment systems.
Experience with orchestration tools like Databricks, Airflow (or similar tools like Snowflake, Dagster , etc.).
Excellent verbal and written communication skills, especially as it relates to technical communications. Ability to present technical analysis — including model behavior, forecast deviations, and recommended actions — to business stakeholders and customer planners.
Benefits:
We understand the importance of work-life balance and wellbeing, which is why we offer flexibility for our teams including: hybrid work, adaptable hours, Summer Flex Fridays, Focus Fridays, and an annual companywide well-being day to promote revitalization and success.
Job Posting Statement:
To protect candidates from falling victim to online fraudulent activity involving fake job postings and employment offers, please be aware our recruiters will always connect with you via @zebra.com email accounts. Applications are only accepted through our applicant tracking system and only accept personal identifying information through that system. Our Talent Acquisition team will not ask for you to provide personal identifying information via e-mail or outside of the system. If you are a victim of identity theft contact your local police department.
AI Technology Statement:
Zebra Technologies leverages AI technology to evaluate job applications using objective, job-relevant criteria. This approach enhances efficiency and promotes fairness in the hiring process. However, every decision regarding interviews and hiring is made by our dedicated team, because we believe people make the best decisions about people. For more on how we use technology in hiring and how we process applicant data, see our Zebra Privacy Policy .
