Amazon.com Services LLC

Data Science, Science, north america stores

DataScientistII

$136–184k Seattle, Washington, United States FULL TIME
Market Sentiment
HIGH DEMAND

Neural analysis suggests this role is
optimal for Mid candidates.

The Brief

“Data Scientist II at Amazon.com Services LLC. Skills: Machine learning, Statistical analysis, Data mining. Analyze complex problems via data analysis. Improve performance of ML recommendation systems”

What You'll Achieve.

Improve performance of ML recommendation systems; Enhance customer experience; Reduce retail returns

Industry & Context.

Data Science, Science, north america stores
Problems you'll solve

Solving unique customer-facing problems; Answer complex problems; Analyze complex datasets; Derive key insights

What They're Looking For.

Must Have

3+ years data querying languages, 3+ years scripting languages, 3+ years statistical/mathematical software, 2+ years data scientist experience, 3+ years machine learning/statistical modeling, Experience applying theoretical models

Nice to Have

Experience in Python, Experience in Perl, Experience in another scripting language, Experience in ML role, Experience in data scientist role, Experience with large technology company, Usage of generative AI tools, Willingness to learn prompting, Willingness to learn evaluation practices, Ability to recognize generative AI opportunities

What You'll Do.

Analyze complex problems via data analysis

Improve performance of ML recommendation systems

Enhance customer experience

Inform roadmap for science

Inform roadmap for product

Analyze complex datasets

Build segmentation models

Improve customer experience

Reduce retail returns

Multi-task between tasks

Integrate disparate datasets

Do business intelligence

Analyze engagement metrics

Present to stakeholders

How You'll Work.

Team & Collaboration

Partner with applied scientists; Partner with data engineers; Partner with product managers; Partner with UX designers; Partner with business partners; Work with cross-functional team members

Communication Scope

Excellent communication skills; Presenting to stakeholders

Full Job Description

Are you passionate about solving unique customer-facing problems in the Amazon scale? Are you excited about utilizing statistical analysis, machine learning, data mining and leverage tons of Amazon data to learn and infer customer shopping patterns? Do you enjoy working with a diversity of engineers, machine learning scientists, product managers and user-experience designers? If so, you have found the right match! Fashion is extremely fast-moving, visual, subjective, and it presents numerous unique problem domains such as product recommendations, product discovery and evaluation. The vision for Amazon Fashion is to make Amazon the number one online shopping destination for Fashion customers by providing large selections, inspiring and accurate recommendations and customer experience. The NAS Shop and Keep (SHAKE) organization empowers customers to make confident, lasting purchase decisions by minimizing purchase anxiety and reducing returns. The NAS SHAKE science team advances this mission by innovating and developing scalable ML solutions tailored to these unique challenges. The team is hiring a Data Scientist who has a solid background in Statistical Analysis, Machine Learning and Data Mining and a proven record of effectively analyzing large complex heterogeneous datasets, and is motivated to grow professionally as a Data Scientist. Key job responsibilities - You will work on our Science team and partner closely with applied scientists, data engineers as well as product managers, UX designers, and business partners to answer complex problems via data analysis. Outputs from your analysis will directly help improve the performance of the ML based recommendation systems thereby enhancing the customer experience as well as inform the roadmap for science and the product. - You can effectively analyze complex and disparate datasets collected from diverse sources to derive key insights, build segmentation models and improve customer experience by reducing retail returns

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