Senior Algorithm Engineer

Senior Algorithm Engineer

1. Job Responsibilities

1. Business Logic Transformation: Thoroughly understand and organize the existing analysts' research data (including structured and unstructured text) and price generation methodology, and abstract business logic into algorithmic problems.

2. Data Mining and Processing: Utilize natural language processing (NLP) and data mining techniques to extract core features and pricing factors from data collected by analysts, ongoing data in the commodity market, and historical research data.

3. Pricing Model Construction: Responsible for designing, developing, and maintaining price generation models (covering time series analysis, machine learning, etc.), ensuring that the daily generated quote data is accurate, reasonable, and highly time-sensitive.

4. Model Iteration and Monitoring: Establish a model effectiveness evaluation system, track the actual performance of daily quotes, and continuously optimize and iterate the model based on market changes and analyst feedback.

Cross-team Collaboration: Maintain close communication with business analysts, data development, and product teams to drive the engineering implementation of pricing algorithms in business systems.

II. Qualifications

1. Bachelor's degree or above in Computer Science, Mathematics, Statistics, Financial Engineering, or related fields.

2. Have a solid foundation in algorithms, and be proficient in commonly used Machine Learning algorithms (such as XGBoost, LightGBM, etc.), time series forecasting models (such as ARIMA, LSTM, etc.), and Deep learning frameworks.

3. Have excellent practical experience in NLP (Natural Language Processing), and be able to proficiently perform text classification, entity recognition, key information extraction, and text summarization.

4. Strong programming skills, proficient in Python, familiar with SQL, and skilled in using toolkits such as Pandas, NumPy, Scikit-learn, PyTorch, or TensorFlow.

5. Possess good data sensitivity and business understanding ability, be able to quickly grasp the essence of problems, and transform complex business requirements into executable algorithmic solutions.

6. Possess excellent logical thinking skills and strong communication and expression abilities, and be able to break down and implement analysts' unstructured methodologies into clear engineering requirements.

3. Bonus Items

1. Candidates with relevant work experience in financial quantitative analysis, commodity pricing, e-commerce dynamic pricing, or operations research optimization are preferred;

2. Familiarity with Knowledge Graph construction, or experience in research report analysis and Agent application implementation using Large Language Models (LLMs), is preferred;

3. Familiarity with big data processing platforms such as Hadoop, Spark, and Flink, and those with certain data engineering capabilities are preferred.