数据科学家
Confirmed on the employer's own hiring board on Oct 31, 2026. First seen by Alion on Oct 9, 2026. DiDi Global scores A on the Alion truth index.
岗位职责 / Responsibilities
1. Establish and iterate the core business indicator system. Adopt causal inference, A/B testing, attribution analysis, time series forecasting and other methodologies to deeply mine user behavior and business rules, and deliver implementable optimization solutions for business growth, operational efficiency and user experience.
2. Lead quantitative modeling for complex business problems; design and deploy machine learning and statistical models (classification, regression, ranking, matching, anomaly detection, user profiling, etc.) to continuously improve model performance and engineering efficiency.
3. Collaborate with product, R&D, operation and algorithm engineering teams to promote the engineering and productization of data science achievements, and quantify business benefits including GMV, retention rate, conversion rate, cost and operational efficiency.
4. Establish the team’s data science methodology and toolchain, precipitate analysis templates, feature libraries and model frameworks, and improve the overall data capability and delivery efficiency of the team.
任职要求 / Requirements
1. Master’s degree or above, major in Statistics, Mathematics, Computer Science, Data Science, Operations Research or other quantitative majors preferred.
2. 5+ years of full-time working experience in internet industry in data science, data analysis and algorithm modeling; proven track record of independently leading end-to-end large-scale data projects from 0 to 1 with measurable business outcomes.
3. Proficient in SQL (mass data query, performance optimization and complex aggregation analysis); skilled in Python (Pandas, NumPy, Scikit-learn); familiar with big data platforms such as Spark and Hive.
4. Solid mathematical and statistical foundation; proficient in statistical modeling, machine learning algorithms, experimental design and causal inference; capable of independently completing feature engineering, model training, evaluation and iteration.
5. Strong business understanding and problem decomposition capabilities; able to convert complex business problems into data science problems and deliver executable solutions.
6. Excellent cross-departmental communication, project promotion and result delivery capabilities; able to deliver data insights and decision support reports to management.
7. Proficiency in English