As a Data Scientist, you will collaborate with business leaders to tackle product challenges using predictive analytics, improve data quality, and align analysis efforts with strategic goals. You will define and implement machine learning models, analyze data trends, and mentor the team in Austin.
Locations - Austin, Texas or major Texas cities who are willing to travel to Austin once a month for team meetings. If not in Texas, you must be willing to relocate.
About the Team
The BI team at Cloudflare is responsible for building and managing the cloud data analytics platform. Our focus is on developing a centralized cloud data analytics platform using open source technologies for internal Business Partners and Machine Learning teams. The team aims to make data more accessible, meet Cloudflare's critical business needs, and provide self-service reporting and analytics tools to support current and new business initiatives.
Data Scientist Responsibilities
Examples of desirable skills, knowledge and experience
About the Team
The BI team at Cloudflare is responsible for building and managing the cloud data analytics platform. Our focus is on developing a centralized cloud data analytics platform using open source technologies for internal Business Partners and Machine Learning teams. The team aims to make data more accessible, meet Cloudflare's critical business needs, and provide self-service reporting and analytics tools to support current and new business initiatives.
Data Scientist Responsibilities
- Partner and align with business leaders, stakeholders, product managers and internal teams to understand the business and product challenges and goals and address them using predictive analytics in a globally distributed environment.
- Understand data landscape i.e tooling, tech stack, source systems etc. and work closely with the data engineering team to improve the data collection and quality.
- Understand business/product strategy and high-level roadmap and align analysis efforts to enable them with data insights and help achieve their strategic goals.
- Strong audience focused presentation and storytelling skills focused on key takeaways in a crisp and concise manner.
- Define, implement, and train statistical, machine learning, deep learning and generative AI models.
- Use software engineering best practices to publish model scores/insights/learnings at scale within the company.
- Ability to define and spot macro and micro levels trends with statistical significance on a regular basis and understand key drivers driving those trends.
- Active role in hiring, growing, and mentoring the data scientist team in Austin.
Examples of desirable skills, knowledge and experience
- M.S or Ph.D in Computer Science, Statistics, Mathematics, or other quantitative fields.
- 3+ years of data scientist experience with proven industry experience in a large scale environment (PBs scale & globally distributed teams)
- Strong experience working with business functions such as Finance, Sales, and Marketing to build models and drive impactful business outcomes (eg: customer and revenue growth)
- 2+ years experience with a fast-growing SaaS business based company is preferred.
- Strong experience in scientific computing using Python.
- Experience with Spark, SQL, Tableau, Google Analytics, BigQuery (or any other Big data/Cloud equivalent) etc.
- Experience working with and processing structured, unstructured, and semi-structured data.
- Strong cross-functional collaboration experience with data engineering and data analysts teams within the function.
- Proven track record of applying data insights and machine learning in order to address business needs and drive revenue.
- Proficiency in large language models and the frameworks necessary for implementing GenAI applications, such as chatbots and related use cases.
- Strong communication and presentation skills catered to different audiences within the company.
- Capable of working closely with business, engineering, and product teams to ensure data initiatives are aligned with business needs.
- Experience in hiring data scientists and establishing team best practices is preferred.
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