Who we are:
Twitter is seeking a senior data scientist to join the Capacity Engineering team to focus on capacity forecasting.
Twitter is what’s happening in the world and what people are talking about right now. It serves hundreds of millions of users across the globe and reaches far more. It relies upon a large infrastructure footprint spanning both private and public cloud totaling many 10’s of millions of CPU cores and many Exabytes of storage. Planning the infrastructure demand is challenging in scale, complexity, and uncertainty: Twitter is relentlessly growing and traffic can spike anytime from a single Tweet or rapidly ramp in global crises.
You’ll work in a team of data scientists and engineers to understand Twitter’s capacity needs and engineer solutions. You’ll focus on the forecasting aspect to model and forecast infrastructure capacity needs across different time horizons under varying scenarios and across multiple infrastructure providers. You’ll collaborate with stakeholders in Engineering, Finance, and Supply to understand what capacity is needed, why it’s needed, and to advise on long term strategic direction.
What you’ll do:
- Forecast capacity demand with confidence intervals at monthly granularity over different time horizons including 3-6 months for immediate allocations, 18+ months for supply chain and financial planning, and 5+ years for infrastructure and business planning. Model inherent uncertainty in developing new products for new markets with new technologies.
- Forecast capacity requirements for planned high traffic/spend events such Black Friday, NYE, SuperBowl, Olympics, and the World Cup.
- Model different scenarios for rapid unplanned traffic spikes and ramps in global site traffic.
- Model different risk scenarios for the availability of capacity including extended regional outages and global supply chain disruption.
- Forecast infrastructure costs on 7-10+ year time horizon for both private and public cloud. Model potential technology improvements and competitive pricing under different hybrid cloud strategies.
- Forecast site traffic including geographic distribution and temporal variation to inform engineering architecture.
- Model and size capacity buffers at different stages to appropriately balance performance and cost under said scenarios.
- Communicate models and forecasts with stakeholders in Engineering, Finance, and Supply.
- Inform company strategy on global infrastructure capacity.
Who you are:
- Passionate to work on challenging forecasting problems with large business impact.
- Adept at translating business needs into concrete requirements.
- Proven ability to drive long term cross-functional projects.
- Biased towards simplicity and action over complexity and deliberation.
- Willingness and ability to lead and mentor other data scientists and engineers on technical matters when necessary.
- Track record of strong product ownership: productionizing, automating, integrating, and documenting models.
- Demonstrated success in modeling and time series forecasting in complex domains with high uncertainty, ideally related to infrastructure.
- Proficient with R or Python.
- 10+ years of relevant industry experience.
- Advanced degree in Economics, Econometrics, Statistics, Operations Research, or related field or equivalent industry experience.
We are committed to an inclusive and diverse Twitter. Twitter is an equal opportunity employer. We do not discriminate based on race, ethnicity, color, ancestry, national origin, religion, sex, sexual orientation, gender identity, age, disability, veteran status, genetic information, marital status or any other legally protected status.
Data Science Masters Program makes you proficient in tools and systems used by Data Science Professionals. It includes training on Statistics, Data Science, Python, Apache Spark & Scala, Tensorflow and Tableau. The curriculum has been determined by extensive research on 5000+ job descriptions across the globe.
What is data science, why is it so popular, and why did the Harvard Business Review hail it as the “sexiest job of the 21st century”? In this non-technical course, you’ll be introduced to everything you were ever too afraid to ask about this fast-growing and exciting field, without needing to write a single line of code. Through hands-on exercises, you’ll learn about the different data scientist roles, foundational topics like A/B testing, time series analysis, and machine learning, and how data scientists extract knowledge and insights from real-world data. So don’t be put off by the buzzwords. Start learning, gain skills in this hugely in-demand field, and discover why data science is for everyone!
What is data science and how can you use it to strengthen your organization? This course will teach you about the skills you need on your data team, and how you can structure that team to meet your organization’s needs. Data is everywhere! This course will provide you with an understanding of data sources your company can use and how to store that data. You’ll also discover ways to analyze and visualize your data through dashboards and A/B tests. To wrap up the course, we’ll discuss exciting topics in machine learning, including clustering, time series prediction, natural language processing (NLP), deep learning, and explainable AI! Along the way, you’ll learn about a variety of real-world applications of data science and gain a better understanding of these concepts through practical exercises.
The book begins by establishing the concept of cloud computing and describing the technological trends, and then discusses cloud computing architecture connotation and key technologies such as computing, storage, network, data, management, access, and security. With abundant project experiences and applications, the book is an essential reference for researchers and industrial engineers in computer science and information management.ed manner
The continuing importance of data analytics is not lost on higher education leaders, who face a multitude of challenges, including increasing operating costs, dwindling state support, limits to tuition increases, and increased competition from the for-profit sector. To navigate these challenges, savvy leaders must leverage data to make sound decisions. In Big Data on Campus, leading data analytics experts and higher ed leaders show the role that analytics can play in the better administration of colleges and universities.
The authors of Big Data with Hadoop MapReduce: A Classroom Approach have framed the book to facilitate understanding big data and MapReduce by visualizing the basic terminologies and concepts. They employed over 100 illustrations and many worked-out examples to convey the concepts and methods used in big data, the inner workings of MapReduce, and single node/multi-node installation on physical/virtual machines.
Is it possible to take advantage of the benefits of data collection–and mitigate risks–for both companies and customers?
Most consumers are not very skilled at evaluating privacy risks; they’re either unable to determine the cost of sharing personal data online or unaware of what they’re sharing. (Doesn’t everyone scroll down without reading to click “I accept”?) Without much intervention from most federal or state-level governments, companies are on their own to define what qualifies as reasonable use. In today’s digital surveillance economy, there are no clear-cut best practices or guidelines. Gathering and using information can help customers–we see that in personalization and autofill of online forms. But companies must act in the best interest of their customers and treat the sensitive information users give them with the ethical care of doctors, lawyers, and financial advisers. The challenges of operating in a digital ecosystem aren’t going away. Customer Data and Privacy: The Insights You Need from Harvard Business Review will help you understand the tangled interdependencies and complexities and develop strategies that allow your company to be good stewards, collecting, using, and storing customer data responsibly.