Data Scientist Ii, Scot - Oss Buying Outcomes

Amazon Amazon · Big Tech · NY +1 · Data Science

Data Scientist II role focused on developing and supporting data science methodologies and models for Amazon's global inventory management and buying programs. The role involves deriving causal inferences, modeling variations, and building tools to drive key decisions in buying and sourcing strategies, with a focus on massive datasets and operational efficiency.

What you'd actually do

  1. Collaborate with product managers and deep learning science and engineering teams to design and implement model solutions for Amazon buying systems
  2. Develop edge case agile models for on-going buying assessments toward the end goal of optimizing buying decisions for millions of products world-wide
  3. Use large datasets or experiments to make causal inferences or predictions
  4. Work with engineers to automate science analysis processes and build scalable measurement solutions
  5. Interpret data, write reports, and make actionable recommendations

Skills

Required

  • 2+ years of data scientist or similar role involving data extraction, analysis, statistical modeling and communication experience
  • 2+ years of data querying languages (e.g. SQL, Hadoop/Hive) experience
  • 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
  • Master's degree in a quantitative field, or Bachelor's degree and 5+ years of a quantitative field such as statistics, mathematics, data science, business analytics, economics, finance, engineering, or computer science experience
  • Experience applying theoretical models in an applied environment

Nice to have

  • Experience in Python, Perl, or another scripting language
  • Experience in a ML or data scientist role with a large technology company

What the JD emphasized

  • exceptional technical expertise
  • exceptional analytical abilities
  • comfortable with ambiguity
  • attention to detail
  • balance analysis with critical thinking and judgement
  • work in a fast-paced and ever-changing environment
  • business impact the findings have had
  • exceptional analytics, statistics, judgment, and communication skills
  • extract insights from data
  • clearly communicate appropriate triggers and actions
  • Drive technical standards and best practices
  • Mentor and provide technical guidance

Other signals

  • developing and supporting best-in-class data science methodologies and models
  • building tools drive key decisions in buying and sourcing strategies
  • deriving causal inferences using observational data
  • model variations related with different buying and cost scenarios