Senior Data Scientist at Amazon
Senior Data Scientist
- Job ID: 1392279 | Amazon Web Services, Inc.
Do you have a passion for diving deep to uncover key insights that drive critical business decisions? If yes, the TAM Operations team is looking for somebody with your enthusiasm and skills to work as part of the team. We are looking for an experienced data scientistwho is curious, driven, and passionate about marketing insights and analytics. If you are looking for a role where you can make a major impact to customers and staff, we want to meet you.
As enterprise customers move towards adopting the cloud for critical workloads, some find they need help to operationalize and optimize their AWS environment. As a member of the TAM organization you will be at the forefront of this transformational technology assisting a global list of companies that are taking advantage of a growing set of services and features to run their mission-critical applications. You will work with leading companies in this space and directly with the engineering teams within AWS developing these new capabilities.
This role will work closely with scientists and engineers to develop and run statistical models to understand how TAM activities drive customer behavior and how customers respond to improvements. You will collaborate directly with finance and business leaders to produce modeling solutions, partner with software developers and data engineers to build end-to-end data pipelines and production code, and have exposure to senior leadership as we communicate results and provide scientific guidance to the business. You will analyze large amounts of business data, automate, and scale the analysis, and develop metrics that will enable us to continually delight our customers worldwide.
As a successful data scientist, you are an analytical problem solver who enjoys diving into data, is excited about investigations and algorithms, can multi-task, and can credibly interface between technical teams and business stakeholders. Your analytical abilities, business understanding, and technical savvy will be used to identify specific and actionable opportunities to solve existing business problems and look around corners for future opportunities.
- Build models and tools using technical knowledge in machine learning, statistical modeling, probability and decision theory, and other quantitative techniques.
- Understand the business reality behind large sets of data and develop meaningful analytic solutions.
- Innovate by adapting to new modeling techniques and procedures.
- Utilizing SQL and code (Python, R, etc.) for analyzing data and building statistical models to solve specific business problems
- Improve upon existing methodologies by developing new data sources, testing model enhancements, and fine-tuning model parameters
- Collaborate with finance, researchers, software developers, and business leaders to define product requirements and provide analytical support
- Communicating verbally and in writing to business customers and leadership team with various levels of technical knowledge, educating them about our systems, as well as sharing insights and recommendations
- Bachelor’s Degree
- 3+ years of experience with data scripting languages (e.g SQL, Python, R etc.) or statistical/mathematical software (e.g. R, SAS, or Matlab)
- 2 years working as a Data Scientist
- Master’s degree or PhD in computer science, statistics, information systems, economics, mathematics or similar
- Multiple years of experience working with large-scale, complex datasets to create/optimize machine learning, predictive, forecasting, and/or optimization models.
- Strong proficiency in SQL
- 2+ years of experience with data querying languages (e.g. SQL), scripting languages (e.g. Python, R), or statistical/mathematical software (e.g. R, SAS, Stata, Matlab, etc.)
- 2+ years of relevant working experience in an analytical role involving data extraction, analysis, and communication
- Practical understanding and hands-on experience with regression modelling (linear and logistic)
- Excellent verbal and written communication skills with the ability to effectively advocate technical solutions to research scientists, engineering teams and business audiences
- Direct experience with both supervised learning methods (linear and logistic regression, time-series modelling, generalized linear models, decision trees, random forests, support vector machines, etc.) and unsupervised learning methods (K-means, hierarchical clustering, association rules, principal components).
- Direct experience analyzing A/B experiments
- Proven ability to convey rigorous technical concepts and considerations to non-experts
- Demonstrable track record of dealing well with ambiguity, prioritizing needs, and delivering results in a dynamic environment
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