Data Scientist II, AWS Managed Operations Data Science (MODS)

Amazon; Missouri; On-site; USD 80,000 - 130,000; Full time

Job summary

An established industry player is seeking a Data Scientist II to join their AWS Managed Operations Data Science team. This role offers the chance to lead data-driven transformation and tackle complex business problems using advanced analytics and machine learning techniques. You will be responsible for the end-to-end data science lifecycle, working with diverse tools such as SQL, Python, and Spark to derive insights that inform strategic decisions. Join a collaborative environment where your contributions will directly impact the operational efficiency of AWS regions, making a significant difference in the tech landscape. If you are passionate about data and eager to drive innovation, this opportunity is for you.

Qualifications

• 3+ years of experience as a data scientist with strong skills in SQL and Python.
• Expertise in machine learning techniques and statistical analysis.

Responsibilities

• Conduct exploratory data analysis to extract insights and identify trends.
• Collaborate with teams to translate data insights into actionable business strategies.

Skills

Data Analysis

Machine Learning

Statistical Modeling

Data Querying (SQL)

Scripting (Python)

Data Visualization

Education

Master's Degree in Statistics

Bachelor's Degree in Statistics

Tools

R

SAS

Matlab

Spark

Hadoop

Job description

Data Scientist II, AWS Managed Operations Data Science (MODS)

Job ID: 2890584 | Amazon Development Center U.S., Inc.

Amazon Web Services (AWS) is the world leader in providing a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world!

Passionate about building, owning and operating massively scalable systems? Want to make a billion-dollar impact? If so, we have an exciting opportunity for you.

The AWS Managed Operations (MO) organization was founded in April 2023, with the objective to reduce operational load and toil through long-term engineering projects. MO is building the best-in-class engineering and operations team that will own the day-to-day operations for AWS Regions; improving the availability, reliability, latency, performance and efficiency to operate AWS regions.

The AWS Managed Operations Data Science (MODS) Team is looking for a Data Scientist to lead the research and thought leadership to drive our data and insight strategy for AWS. You will be expected to serve as a Full Stack Data Scientist. You will be responsible for driving data-driven transformation across the organization. In this role, you will be responsible for the end-to-end data science lifecycle, from data exploration and feature engineering and ETL to model development. You will leverage a diverse set of tools and technologies, including SQL, Python, Spark, Hugging Face and various machine learning frameworks, to tackle complex business problems and uncover valuable insights.

Your product analytics research will provide direction on the technology strategy of the Managed Operations organization. Your Decision Science artifacts will provide insights that inform AWS' Operations and Site Reliability Engineering teams. You will work on ambiguous and complex business and research science problems at scale. You are comfortable working with cross-functional teams and systems.

This position requires that the candidate selected be a U.S. citizen.

Key job responsibilities

1. Collaboration & Cross Functional Relationships: Interact with business and software teams to understand their business requirements and operational processes.
2. Data Exploration and Analysis: Conduct in-depth exploratory data analysis to understand the structure, quality, and patterns within complex datasets. Apply statistical and machine learning techniques to extract insights, identify trends, and uncover hidden relationships in the data.
3. Business Insights and Recommendations: Frame business problems into scalable solutions; translate complex data insights and model outputs into actionable recommendations that address the organization's strategic objectives.
4. Data Pipeline and Infrastructure: Contribute to the design and implementation of data pipelines, data lakes, and other data infrastructure components to support the organization's data-driven initiatives.
5. Metric Development and Monitoring: Define and develop advanced, customized metrics and key performance indicators (KPIs) that capture the nuances of the organization's strategic objectives and operational complexities. Continuously monitor and evaluate the performance of metrics.
6. Prototype models: Use high-level modeling languages such as R or in software languages such as Python. A software team will be working with you to transform prototypes into production.
7. Documentation & Continuous Improvement: Create, enhance, and maintain technical documentation.

BASIC QUALIFICATIONS

- 3+ 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
- 3+ years of machine learning/statistical modeling data analysis tools and techniques, and parameters that affect their performance experience
- Knowledge of relevant statistical measures such as confidence intervals, significance of error measurements, development and evaluation data sets, etc.
- Master's Degree in Statistics, Applied Math, Operations Research, Economics, or a related quantitative field with 2+ years' experience in Data Science or related Science discipline, OR, Bachelor's Degree in Statistics, Applied Math, Operations Research, Economics, or a related quantitative field with 5+ years' experience in Data Science or related Science discipline

PREFERRED QUALIFICATIONS

- 6+ years of data scientist experience
- 4+ years of machine learning, statistical modeling, data mining, and analytics techniques experience
- Experience with data scripting languages (e.g., SQL, Python, R, or equivalent) or statistical/mathematical software (e.g., R, SAS, Matlab, or equivalent)
- Experience with clustered data processing (e.g., Hadoop, Spark, Map-reduce, and Hive)
- Experience in a ML or data scientist role with a large technology company

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