Jobs

Data Scientist , JCI Measurement and Optimization Science Team
Tokyo, 13, JPN
·
On-site
·
Full-time
·
2w ago
We are seeking a talented, customer-focused Data Scientist to join our JCI Measurement and Optimization Science Team (JCI MOST), with a charter to identify the right data, build prediction models and optimization capabilities to help inbound service teams make data driven decisions.
This role requires an individual with exceptional data manipulation skill, machine learning, LLM, and excellent cross-functional collaboration skills, business acumen, and an entrepreneurial spirit to solve business challenges using data and science.
We are looking for an experienced innovator who is a self-starter, comfortable with ambiguity, demonstrates strong attention to detail, and thrives in a fast-paced, data-driven environment.
- Key job responsibilities
- Partner with business stakeholders to deeply understand inbound service programs in Japan and frame ambiguous business problems as science problems and solutions.
- Perform data analysis and build data pipelines to drive business decisions.
- Invent novel science solutions and develop prototypes.
- Review and guide science solutions across the team.
- Publish and socialize your and the team's research across Amazon and external avenues as appropriate
- Leverage industry best practices to establish repeatable science practices, principles & processes.
Basic Qualifications
- 2+ years of data scientist experience
- 3+ years of data querying languages (e.g. SQL), scripting languages (e.g. Python) or statistical/mathematical software (e.g. R, SAS, Matlab, etc.) experience
- Master's degree in Science, Technology, Engineering, or Mathematics (STEM), or experience working in Science, Technology, Engineering, or Mathematics (STEM)
Preferred Qualifications
- Ph.D. in Science, Technology, Engineering, or Mathematics (STEM)
- Knowledge of machine learning concepts and their application to reasoning and problem-solving
- Experience in Python, Perl, or another scripting language
- Experience in a ML or data scientist role with a large technology company
- Experience in defining and creating benchmarks for assessing GenAI model performance
- Experience working on multi-team, cross-disciplinary projects
- Experience applying quantitative analysis to solve business problems and making data-driven business decisions
- Experience effectively communicating complex concepts through written and verbal communication
Our inclusive culture empowers Amazonians to deliver the best results for our customers. If you have a disability and need a workplace accommodation or adjustment during the application and hiring process, including support for the interview or onboarding process, please visit https://amazon.jobs/content/en/how-we-hire/accommodations for more information. If the country/region you’re applying in isn’t listed, please contact your Recruiting Partner.
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About Amazon

Amazon
PublicAmazon.com, Inc. is an American multinational technology company engaged in e-commerce, cloud computing, online advertising, digital streaming, and artificial intelligence.
10,001+
Employees
Seattle
Headquarters
$1.5T
Valuation
Reviews
2.9
10 reviews
Work-life balance
2.8
Compensation
3.7
Culture
2.5
Career
2.3
Management
2.1
35%
Recommend to a friend
Pros
Good pay and compensation
Strong benefits package
Flexible scheduling options
Cons
Poor management and leadership
Limited growth and promotion opportunities
High stress and demanding work environment
Salary Ranges
4 data points
Junior/L3
L2
L3
L4
L5
L6
M3
M4
M5
M6
Mid/L4
Principal/L7
Senior/L5
Staff/L6
Director
Junior/L3 · Data Scientist L4
0 reports
$181,968
total per year
Base
-
Stock
-
Bonus
-
$154,672
$209,264
Interview experience
10 interviews
Difficulty
3.7
/ 5
Duration
21-35 weeks
Offer rate
20%
Experience
Positive 10%
Neutral 10%
Negative 80%
Interview process
1
Application Review
2
Recruiter Screen
3
Online Assessment
4
Technical Phone Screen
5
Onsite/Virtual Loop
6
Team Matching
7
Offer
Common questions
Coding/Algorithm
System Design
Behavioral/STAR
Leadership Principles
Technical Knowledge
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