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Popular mid-level Data Scientist role with broad ML requirements and a strong global brand drives high competition.
Core ML skills are transferable, but forecasting and market-mix expertise favor CPG/retail backgrounds.
Mandatory 4–6 years, expert Python, supervised ML experience and specific modeling skills enforce high shortlisting strictness.
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Build and deploy machine learning models across use cases like demand forecasting, text classification, operational analytics, logistics, price optimization, ecommerce, and market mix modeling.
Develop novel analytical solutions by integrating external datasets and engineering features to create scalable data science models.
Collaborate with cross-functional and technical teams to innovate, automate processes, and advocate data-driven decision making.
Contribute to data science standards, frameworks, and productivity tools for the enterprise.
Bachelor's degree in Technology, Engineering, or Science.
4-6 years of analytics experience with supervised machine learning models.
Experience working in Agile development environments with sprint cycles and daily stand-ups.
Technical competency in Python (expert), predictive modeling or forecasting or market mix modeling or Bayesian methods or optimization (two required), intermediate R & SQL, cloud platforms (preferably Google Cloud), and data sourcing from big query, flat files, or cloud storage.
Experienced data scientist with proven ability to apply machine learning to diverse business domains including forecasting and optimization.
Strong collaborator able to communicate data-driven insights effectively to cross-functional teams and influence business decisions.
Hands-on technical skills with Python and cloud-based data systems, comfortable innovating and automating within an Agile environment.