崗位職責(zé):
1. 研究自然語言處理,預(yù)訓(xùn)練模型,文本生成等相關(guān)領(lǐng)域的主流算法;
2. 負(fù)責(zé)整個項(xiàng)目的建模實(shí)驗(yàn)到產(chǎn)品落地;
3. 負(fù)責(zé)使用*前沿的算法落地到實(shí)際項(xiàng)目中;
4. 跟進(jìn)前沿NLP技術(shù);
任職要求:
1. 計(jì)算機(jī)/數(shù)學(xué)/統(tǒng)計(jì)學(xué)/模式識別相關(guān)專業(yè),本科及以上學(xué)歷,5年以上相關(guān)工作經(jīng)驗(yàn):熟悉NLP (包括信息抽取,預(yù)訓(xùn)練模型,自然語言生成語義分析,智能問答,摘要生成) 和機(jī)器學(xué)習(xí)的理論基礎(chǔ);
2. 熟悉本研究領(lǐng)域的*新研究成果,公開數(shù)據(jù)集和相關(guān)的開源系統(tǒng);
3. 具有扎實(shí)的數(shù)據(jù)結(jié)構(gòu)和算法設(shè)計(jì)基礎(chǔ),優(yōu)秀的編程能力,精通Python;熟悉pytorch,TensorFlow 等至少一種開源深度學(xué)習(xí)框架;
4. 具備獨(dú)立算法編寫、優(yōu)化、調(diào)試、運(yùn)行和部署模型代碼的能力;
5. 有很強(qiáng)的自學(xué)能力和獨(dú)立思考能力,善于思考和表達(dá)自己的想法:同時又具備良好的團(tuán)隊(duì)合作精神;
6. 具備NLP方向中英文論文熟練閱讀的能力;
7. 有團(tuán)隊(duì)管理經(jīng)驗(yàn) (必備)。
更新于 2025-12-31
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As a Data Scientist, you will design, develop, deploy predictive models machine learning solutions to solve complex business challenges. You’ll collaborate with data engineers, business analysts, clients to translate requirements scalable data products, from concept to production.
This role is ideal fsomeone passionate about statistical modelling, machine learning, deriving actionable insights from large datasets.
Key Responsibilities
1. Develop robust data models conduct feature engineering to improve model performance
2. Perform exploratory data analysis (EDA) to uncover patterns, trends, insights
3. Collaborate with data engineers to build optimize data pipelines fmodel training inference
4. Evaluate model performance using appropriate metrics validation techniques
5. Deploy models production environments in collaboration with ML engineers DevOps teams
6. Communicate findings recommendations to technical non-technical stakeholders through visualizations reports
7. Stay up to date with emerging trends in AI/ML apply best practices in model interpretability, fairness, MLOps
Requirements
1. Bachelor’s Master’s degree in Data Science, Computer Science, Statistics, Mathematics, a related field
2. 3+ years of experience in data science machine learning roles
Strong proficiency in Python (e.g., pandas, scikit-learn, NumPy, statsmodels)
3. Experience with data modelling, feature engineering, statistical analysis
4. Familiarity with MLOps tools (e.g., MLflow, Kubeflow, Airflow) cloud platforms (AWS, GCP, Azure)
5. Knowledge of SQL database systems
6. Experience with version control (Git) collaborative development workflows
7. Strong analytical thinking problem-solving skills
更新于 2025-12-03
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