Senior Data Scientist, AWS Support Capacity Planning

Key Skills

Apache Hadoop, C Programming, C++, Data science techniques, Java Programming, Machine learning techniques, MapReduce, Python Programming, PyTorch, R Programming, Scala Programming, SQL, TensorFlow

Job Description

Do you have proven analytical capabilities to identify business opportunities, develop predictive models and optimization algorithms to help us build state of the art Support organization?

At Amazon, we are working to be the most customer-centric company on earth. To get there, we need exceptionally talented, bright, and driven people. We set big goals and are looking for people who can help us reach and exceed them. Amazon Web Services (AWS) is one of the world’s most comprehensive and broadly adopted cloud platform, offering over 200 fully featured services from data centers globally. Amazon Web Services, Inc. provides services for broad range of applications including compute, storage, databases, networking, analytics, machine learning and artificial intelligence (AI), Internet of Things (IoT), security, and application development, deployment, and management.

AWS Support Engineering Capacity Planning team is looking for a strong, talented Data Scientist to model contact and volume forecasting, discovering insights and identifying opportunities through the use of statistics, machine learning, and combinatorial optimization problems to drive business and operational improvements. You are passionate about building solutions that will help drive a more efficient operations network and optimize cost. In this role, you will partner with data engineering, tooling team, operations, training, workforce management and finance teams, driving optimization and prediction solutions across the network influencing the long-term strategy of the business.

We are looking for an experienced and motivated Data Scientist with proven abilities to build and manage modeling projects, forecasting solutions, identify data requirements, build methodology and tools that are statistically grounded.You are an expert in the areas of data science, forecasting, optimization, machine learning and statistics, and is comfortable facilitating ideation and working from concept through execution. You are customer obsessed, innovative, independent, results-oriented and enjoys working in a fast-paced growing organization. An interest in operations, process improvement is helpful. The ability to embrace this ambiguity and work with a highly distributed team of experts is critical. While this is a small team, there is opportunity to own globally impactful work and grow your career in technical, programmatic or people leadership. You will likely to work in Python or R, building forecasting, predictive and optimization models. Your problem solving ability, knowledge of data models and ability to drive results through ambiguity are more important to us.

About the team

About Us\

Inclusive Team Culture

Here at AWS, we embrace our differences. We are committed to furthering our culture of inclusion. We have ten employee-led affinity groups, reaching 40,000 employees in over 190 chapters globally. We have innovative benefit offerings, and host annual and ongoing learning experiences, including our Conversations on Race and Ethnicity (CORE) and AmazeCon (gender diversity) conferences. Amazon’s culture of inclusion is reinforced within our 16 Leadership Principles, which remind team members to seek diverse perspectives, learn and be curious, and earn trust.

Work/Life Balance

Our team puts a high value on work-life balance. It isn’t about how many hours you spend at home or at work; it’s about the flow you establish that brings energy to both parts of your life. We believe striking the right balance between your personal and professional life is critical to life-long happiness and fulfillment. We offer flexibility in working hours and encourage you to find your own balance between your work and personal lives.

Mentorship & Career Growth

Our team is dedicated to supporting new members. We have a broad mix of experience levels and tenures, and we’re building an environment that celebrates knowledge sharing and mentorship. We care about your career growth and strive to assign projects based on what will help each team member develop into a better-rounded professional and enable them to take on more complex tasks in the future.

BASIC QUALIFICATIONS

• A Bachelor or Masters Degree in a highly quantitative field (Computer Science, Machine Learning, Operations Research, Statistics, Mathematics, etc.) or equivalent experience
• 6+ years of industry experience in predictive modeling, forecasting, data science and data analysis
• Demonstrated experience using one or more data science programming language (Python, R, etc).

PREFERRED QUALIFICATIONS

• Experience processing, filtering, and presenting large quantities (Millions of rows) of data
• Experience with statistical analysis, data modeling, machine learning, optimizations, regression modeling and forecasting, time series analysis, data mining, and demand modeling
• Experience applying various machine learning techniques, and understanding the key parameters that affect their performance
• Experience with Predictive analytics (e.g., forecasting, time-series, neural networks) and Prescriptive analytics (e.g., stochastic optimization, bandits, reinforcement learning)
• Experience in Data Analysis programming such as R, Python, Scala, Spark, etc.
• Experience in cloud deployment tools such as AWS S3, EC2, Docker, Kubernetes etc.
• Experience in Linux/Unix shell scripting.
• Proficiency with TABLEAU or other web based interfaces to create graphic-rich customizable plots, charts data maps etc.
• Able to write SQL scripts for analysis and reporting (Redshift, SQL, MySQL)
• PhD in Artificial Intelligence, Computer Science, Statistics, Applied Math or a related field
• Previous experience in ML, data scientist or optimization engineer role with a large technology company
• Familiarity with the processes used in Amazon fulfillment network
• Experience in an operational environment developing, fast-prototyping, piloting and launching analytic products
• Experience in writing academic-styled papers for presenting both the methodologies used and results for data science projects.
• Ability to develop experimental and analytic plans for data modeling processes, use of strong baselines, ability to accurately determine cause and effect relations
• Experience in creating data driven visualizations to describe an end-to-end system
• Excellent written and verbal communication skills. The role requires effective communication with colleagues from computer science, operations research and business backgrounds.