What you need to know about these roles on a data science team

Translator|Li Rui

Reviewer|Sun Shujuan

The world-renowned streaming service Netflix changed its five-star rating system to "thumbs up" in 2017 Simple rating system of "thumbs down" (like) and "thumbs down" (dislike). The system can recommend movies based on percentage matches, but some people find this objectionable. So how do you reduce all the nuances in the art of cinema to a primitive binary reaction? Give it a high rating, but it's not necessarily a movie they'd actually enjoy watching. At least that's what the data says. So how does data analytics work at a company like Netflix? What is the role of the data science team?

NETFLIX FEEDBACK SYSTEM

Gibson Biddle is the former vice president and chief product officer of Netflix. Speaking about consumer insights, he explained the unexpected customer behavior that led to changes in the entire rating system. When converted to a percentage match, Netflix said that while viewers might enjoy watching the Adam Sandler comedy, the rating might be as low as three stars; while viewers might feel better about watching Schindler's List. It's good and gives it a five-star rating, but that doesn't add to its overall enjoyment, and keeping subscribers happy is crucial to Netflix. Therefore, they simplified the feedback system to avoid bias. But these customer insights are impressive in their own right, and they would not be possible without a culture that promotes data use and a strong data infrastructure. In technical terms, this is called a data-driven organization.​

Data-Driven Organization

People may have heard the popular term “data-driven” many times, but what does it really mean? Netflix alone records more than 700 billion events every day, from logging in and clicking movie thumbnails to pausing videos and turning on subtitles. All this data is available to users. Anyone can access it using visualization tools like Tableau or Jupiter, or they can access it through the Big Data Portal - an environment that allows users to inspect reports, generate reports or query any information they need. This data is then used to make business decisions, from smaller thumbnails (such as which thumbnails to display) to enterprise decisions (such as which shows Netflix should invest in and launch next).

Netflix is ​​not the only company that is a data-driven organization. It is estimated that about 97% of Fortune 1000 companies invest in projects such as artificial intelligence and big data. Here's a look at real data infrastructure technology and the data engineers who make it work.

Data Infrastructure Technology

To describe how data infrastructure works, technologists borrow the term "pipelines" that typically transport liquids or gases. A data pipeline has its own starting point, ending point, and intermediate stations. So this is a very apt metaphor. The source of the data could be anything from clicking a hold button and refreshing a page to a recorded conversation with customer support, from a vehicle tracking device to a turbine vibration sensor in a power plant. In today's world, it's actually hard to say what can't be generated that can tell people something even without data.

Once a data item is generated, it travels along the pipeline to the staging area. This is where all raw data is saved. The raw data is not ready for use yet. You have to be prepared. If not done, gaps must be filled in, its format changed, or data from different sources combined to obtain a more nuanced view. Once these operations are completed, you will have structured and clean data. All these operations are performed automatically. They are described in two words.

Extraction: Extract data from the data source and send it to the staging area.

Transformation: Prepare data for consumption and load to push the prepared data further into ETL.

• All prepared data goes into another storage, the data warehouse.
• Data Warehouse

Unlike the staging area, the data warehouse is where all stored records are structured and ready for use. Just like a library's classification system, the warehouse's information can be queried, visualized and downloaded. For this, business intelligence (BI) software is a must. It presents data to the end user.

Data Listing and Business Analysts

Data Listing and Business Analysts are people who perform basic tasks. They access data, explore it, visualize it, and try to make business sense. For example, are the company’s marketing campaigns successful? What are the worst performing channels? They act like a sensory system, supporting businesses with historical data and providing insights to management and ultimately decision makers.

Data Engineer

The data engineer is responsible for building the entire pipeline. Most technicians are well versed in what is called "piping." Move data from source to destination through pipelines, transforming it along the way. They design the pipeline architecture, set up the ETL process, configure the warehouse, and connect it with reporting tools. For example, Airbnb has about 50 data engineers. The company may sometimes encounter a more elaborate approach that involves some additional rules. For example, data quality engineers ensure that data is captured and transformed correctly. Having biased or incorrect data is too costly when trying to draw decisions from it. There may be a separate engineer responsible only for ETL. Additionally, business intelligence developers only focus on integrating reporting and visualization tools. However, reporting tools don’t grab the headlines, and data engineer isn’t the best job of the 21st century, but machine learning and data scientist probably are.

Machine Learning and Data Scientists

It is well known that data scientists are particularly good at collecting data and answering complex questions about the data, such as what will the company's revenue be next quarter? When will the car scheduled with Uber arrive? How likely is it to like Schindler's List and Uncut Gems?

There are actually two ways to answer these questions. Data scientists work with business intelligence tools and warehouse data just like business analysts and data analysts. So, they will get the data from the warehouse. Sometimes data scientists use a data lake: another type of storage for unstructured fraud data. They will create a forecast model and come up with forecasts that can be used by management. It's good for one-time reporting of revenue estimates, but it's not helpful for predicting arrival times of cars for Uber appointments.

The real value of machine learning is that production models can work automatically and regularly generate answers to complex questions, sometimes thousands of times per second, and the things they can handle are much more complex.

Producing Machine Learning Models

In order for the model to work, infrastructure is also required. Sometimes this is a big problem. Data scientists explore data in data warehouses and data lakes, conduct experiments on it, select algorithms, and train models to produce final machine learning code. This requires a deep understanding of statistical databases, machine learning algorithms, and subject areas.

Josh Wills, the former head of data engineering at SLAC, said on Twitter, "Data scientists are people who are better at statistics than any software engineering."

For example, orderers use ubereats software order. Once the user confirms the order, the application must estimate the delivery time, the orderer's location, the restaurant and the order data to be sent to a server where a delivery prediction machine learning model is deployed. But these data are not enough. The model also pulls additional data from a separate database that contains average restaurant prep times and other details. Once all the data is available, the model returns predictions to the orderer. However, the process does not end there. The predictions themselves are saved in a separate database. It will be aimed at monitoring model performance and exploring the model through analysis tools so that it can be updated later. All this data ends up in data lakes and data warehouses.

In fact, the UberEats food ordering service alone uses hundreds of different models working simultaneously to score recommendations, rank restaurants in searches, and estimate delivery times.

Conclusion

Adam Waxman, the core technology leader of Foursquare, believes that there will no longer be data scientists or machine learning engineers in the future, because with the automation of model training and the continuous construction of production environments, Many data scientist jobs will become common functions in software development.

Original title: ​​Roles in Data Science Teams​​, author: Anomi Ragendran

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