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Manager, Data Scientist

Zebra Technologies
Karnataka, IND
Full-time
Hybrid
Discovered 1 months ago
Data sciencePeople managementMentoring and coachingCode and model reviewMachine learning best practicesCustomer analytics engagement

Full Job Posting

Role overview

The position is Manager, Data Science, in the Workcloud SaaS Professional Services department.

The description gives the technology stack as Databricks Unified Analytics Platform, Microsoft Azure Lakehouse Platform, and modern GenAI and agentic AI stacks.

The role leads a data science pod, enterprise client analytics relationships, SaaS product strategy, and adoption of GenAI and agentic AI.

The business serves retail and CPG supply chains with forecasting and decision-management products.

People leadership and delivery

  • Manage, mentor, and coach two to three junior or associate data scientists and interns; conduct technical reviews and performance reviews.
  • Lead analytics engagements for enterprise retail and CPG clients and translate ambiguous problems into roadmaps, scoped deliverables, and managed project commitments.
  • Explain predictive and prescriptive models in business terms and communicate customer value.

GenAI and agentic AI transformation

  • Design and roll out GenAI and agentic AI capabilities to improve professional services productivity and delivery speed.
  • Architect agentic workflows for data discovery, schema mapping, and ETL migration to reduce customer onboarding time.
  • Apply LLMs and foundation models to forecast validation, root-cause generation, promotional cannibalization modeling, and cold-start demand intelligence.

Product innovation

  • Identify recurring customer requirements and algorithmic customizations and partner with Product Management and Core Engineering to turn them into reusable product capabilities.

Role qualifications

  • Requires data science leadership, team management, coaching, and rigorous review of code, models, and methods.
  • Requires enterprise customer engagement and the ability to scope analytics work, manage expectations, and communicate complex model behavior clearly.
  • Calls for leadership in GenAI and agentic AI adoption and familiarity with the stated cloud lakehouse and AI technology stack.

Product and domain context

The platform's time-series demand forecasting supports inventory allocation and replenishment, assortment planning and store clustering, pricing and promotions, predictive ordering, and size and pack optimization.

Additional applications include intelligent order promising, retail shrink and fraud, supply-chain anomaly detection, and workforce and labor scheduling optimization.

Workplace

  • Location is stated as hybrid in Bangalore.

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