Data Analyst:
Work Content:
a. Temporary data analysis, such as double 11 promotion activity analysis; product traffic conversion, product process optimization analysis, and so on;
b. Reporting requirements analysis - such as the common daily, weekly, monthly, quarterly, annual reports, product reports, traffic conversion reports, business analysis reports, KPI reports, and so on;
b. Reporting requirements analysis - such as common daily, weekly, monthly, quarterly, annual, product reports, traffic conversion reports, business analysis reports, KPI reports, and so on;
b. reports, KPI reports and so on;
c. Analysis of business topics:
Precise marketing analysis (user profile analysis, marketing object analysis, marketing strategy analysis, marketing effect analysis);
Wind control analysis (strategy analysis, anti-fraud analysis, credit status analysis);
Market research analysis (industry analysis, competitor analysis, market analysis, price analysis, channel analysis, decision analysis, etc.);
Tools and skills:
Tools: R, Python, SAS, SPSS, Spark, X-Mind, Excel, PPT
Skills: need to master the SQL database, probability statistics, commonly used algorithmic models (classification, clustering, correlation, prediction, etc., one or two most typical algorithms for each type of model). model of one or two of the most typical algorithms), analysis report writing, business sensitivity, etc.;
Data Mining Engineer:
Work Content:
a. User Basic Research: User Life Cycle Portrayal (Entry, Growth, Maturity, Decline, and Churn), User Segmentation Model, User Value Model, User Activity Model, User Preferences Identification Model, User Identification Model, and User Preferences Identification Model, User Segmentation Model, User Value Model, User Activity Model, User Willingness Identification Model, User Preference Identification Model, User Preferences Identification Model, and User Preferences Identification Model. user preference identification model, user churn early warning model, user activation model, etc.
b. Personalized recommendation algorithms: recommendation based on collaborative filtering (USERBASE/ITEMBASE), recommendation based on content, recommendation based on the Apriot algorithm based on association rules, recommendation based on the popular regions, seasons, commodities, and crowds, etc.
c. Risk control models: Malicious registration model, off-site identification model, fraud identification model, high-risk member model,
e-commerce field (speculation model, brush model, professional bad evaluator model, false shipment model, anti-fraud model)
financial field (fraud scoring model, credit scoring model, collection model, false bill identification model, etc.)
d.Product Knowledge Base: product clustering classification model, product quality scoring model, contraband identification model, counterfeit goods identification model, etc.
e. Text mining, semantic recognition, image recognition, and so on
Tools and Skills:
Tools: R, Python, SAS, SPSS, Spark, Mlib, etc.
Skills: need to master SQL database, Probability statistics, principles of machine learning algorithms (classification, clustering, correlation, prediction, neural network, etc.), model evaluation, model deployment, model monitoring;
Data Product Manager:
Job Description:
a. Big data platform construction, make access to data, with data becomes a breeze; build a perfect indicator system, to achieve the whole process of monitoring the business, Improve decision-making efficiency, reduce operating costs, and increase revenue;
b. Data demand analysis, forming data products, internally improving efficiency and controlling costs, externally increasing revenue generation, and ultimately realizing data value realization;
c. Typical big data products: big data analysis platform, personalized recommendation system, precision marketing system, advertising system, credit scoring system (e.g., sesame score), member data service system (e.g., Sesame score), and member data service system (e.g., Sesame score). scoring), member data service system (such as data vertical), and so on;
Tools and skills:
Tools: In addition to mastering the data analysis tools, you also need to master like prototyping tools Auxe, drawing structure flow of X-Mind, visio, Excel, PPT and so on
Skills: you need to master SQL database, product design, and at the same time, familiar with the commonly used data product framework
Skills: you need to master SQL database, product design, at the same time
Data R&D Engineer:
Work content:
a. Data acquisition work such as big data collection, log crawler, data reporting
b. Big data cleaning, conversion, calculation, storage, presentation, etc.
c. Big data application development, visualization development, report development, etc.
Tools and skills:
Tools: hadoop, hbase, hive, kafaka, sqoop, java, python, etc.
Skills: need to master the database, log collection methods, distributed computing, real-time computing and other technologies