拉杰夫古普塔
验证专家 in 工程
人工智能(AI)开发者
Rajeev is passionate about data and machine learning and has more than five years of experience in data science projects across numerous industries and applications. 他目前专注于TensorFlow等尖端技术, Keras, 深度学习, 以及大部分Python数据科学堆栈. Rajeev使用这些技能解决了NLP中的许多实际业务问题, 图像处理, 和时间序列域.
Portfolio
Experience
Availability
首选的环境
Google Cloud, Jupyter笔记本, Spyder, Git
最神奇的...
...project I've implemented was a NLP attention boosted sequential inference model to automate one of the business processes.
工作Experience
数据开发人员
Availyst有限责任公司
- 与美国一家食品聚合初创公司合作,研究数据工程和数据抓取, 使用Python数据科学堆栈, Jupyter笔记本, 和AWS服务.
- 处理了推荐引擎为用户推荐的一种食物和餐厅.
- 使用Python开发抓取应用程序,并使用AWS服务进行部署.
独立顾问-数据科学家
JSS信息技术企业孵化器
- 在JSS信息技术企业孵化器担任数据科学导师.
- 帮助小公司和初创公司利用他们的数据.
- 使用机器学习创建预测模型.
- 用神经网络进行自然语言处理.
- 开发分类和回归算法.
- 实现时间序列预测.
- 开发图像检测与深度学习.
数据科学家-金融科技项目
福布斯媒体- Q.ai
- 管理商业智能团队,作为客户的高级数据科学家.
- 做过量化研究员, using advanced forms of quantitative techniques and artificial intelligence to generate investment recommendations across multiple asset classes, 包括股票, ETFs, 选项, 和cryptocurrencies.
- 使用Dash为增长、营销和领导团队创建了一个仪表板, Plotly, 和表.
高级数据科学家和数据分析师
全球一流欧博体育app下载公司
- 担任客户及其团队的数据科学家和高级分析师.
- 曾为美国一家大型时装零售商进行需求空间细分.
- 将600万客户数据映射到需求空间段.
数据科学家
美国一家电信和媒体公司
- 与美国一家电信和媒体公司合作,识别假新闻.
- 建立了两个模型来识别文章中的讽刺和量化谬误.
独立顾问-数据科学家
IBM
- 曾为IBM美国公司优化其美国设施租赁以运行其运营.
- 开发Python模型以提高设施利用率, reduce facility operations cost and reduce lease cost along with number of business constraints.
独立顾问-数据科学家
AbbVie公司.
- Worked closely with the C-level executive and product management team to analyze the survey and produced data/reports.
- Helped the product team and executive team to make more informed decisions—increasing market share through the identification of new opportunity, 瞄准细分市场,设计巧妙的解决约束的新方法.
独立顾问-数据科学家
Newristics
- Developed a Python app which uses natural language processing with deep neural networks sequence to sequence learning to automate business process.
- 降低了业务运营成本.
数据科学家
新加坡Sopra Steria酒店
- 与陆路运输管理局合作, Singapore to implement the vision to convert the city into a digital and intelligent one to improve the efficiency of services for the citizens, 使用机器学习, 预测建模, 数据挖掘.
数据科学家
Steria印度
- Built a recommendation system for an eCommerce site; it recommended the best possible items to buy based on customer history and collaborative filtering.
- Helped with customer churn prediction by developing a classification algorithm for a retail bank to identify customers likely to churn balances in the next quarter by at least 50% vis-a-vis current quarter.
- Created a classification algorithm for a retail bank to improve sales from existing customers by cross-selling one of its product, 个人贷款(客户交叉销售).
技术项目经理
Steria印度-巴克莱银行
- 在五年内建立约4300万英镑的客户留存业务效益, 节约成本, 以及新的商业机会,预计成本约为1200万英镑.
- Acted as a vital member of the steering committee that identified user needs and developed customized solutions for around 250,000家巴克莱卡收购商.
- 领导一个包括解决方案架构师在内的147人的项目团队, 设计师, 开发人员, and testers spread across multi-geographical locations through the entire project development life cycle.
- 始终保持在每月资源和预算预测的5%左右.
- Recognized as problem solver within a team of 22 project managers in the portfolio of annual spend over £70 million.
Experience
IBM
我开发了Python整数编程算法来解决这个问题. 考虑业务约束使这个问题变得有趣和独特. I parameterized the optimization period (the period to look into the future) in the algorithm to provide multiple solutions. 客户特别喜欢这个特性.
技术:Python, plot,线性编程,包装纸浆
Newristics
I automated the message scorer process where a team compares the new message against the old one and analyzes it to rate how closely it depicts the heuristic.
然后使用文本清理对文本数据进行预处理, 文本归一化, 并生成归一化数据的一元图. I built two main models to solve this problem: XGBoost and deep neural network seq-to-seq learning.
对于XGBoost,我创建了大约900个特性(分为三个部分).
•NLP基本特征:信息的字数/比例/字符数, 单元/双单元的TF-IDF, TF-IDF相似性, 等等......
• Word embedding—similarity of self/pre-trained Word2vec/GloVe-weighted average embedding vectors (TF-IDF as weight), etc.
• Graph—degree of nodes, the intersection of neighbors, k-core/k-clique, degree of separation, etc.
I used the 深度学习 seq-to-seq model to enhance the sequence inference neural network architecture.
技术:Python, LSTM, gensim, GloVe, SpaCy, NLTK, Scikit-learn, TensorFlow, Keras, Jupyter笔记本, Git, 谷歌云平台
AbbVie公司.
We interviewed 119 physicians about HCV regiment attributes which impact the market driver, 55名医生关心病人的治疗, and 60 physicians about sales rep interaction and their impression about the message and interaction.
I worked closely with the C-level executive and product management team to analyze the survey and produced data/reports. This helped the product team and executive team to make more informed decisions—increasing market share through the identification of new opportunity, 目标细分市场, 并设计出巧妙的解决约束的新方法.
技术:Python, R, Plotly, Matplotlib, 回归, 集群, Association Rule
H进行分类&E染色乳腺癌组织学图像
技术:Python 3, Keras, NumPy, Pandas, SciPy, Scikit-learn
啤酒公司sku级的需求预测
In order to plan its production and distribution as well as help wholesalers with their planning, it is important for them to have an accurate estimate of demand at SKU level (34) for each wholesaler (60).
数据:使用60家机构、34家sku四年的数据进行预测.
•价格促销(美元/升):价格, sales, 以单位单位月为单位,按每百升的美元价值进行促销
•历史销量(百升):以代理商-库存-月为单位的销售数据
•天气(摄氏度):一个机构月份的平均最高温度
•行业苏打水销售额(百升):行业苏打水销售额
•事件日历:事件细节(体育、嘉年华等)
•行业量(百升):行业实际啤酒量
• Demographics: Demographic details (yearly income in dollars); used deep neural networks sequence to sequence learning for demand prediction
基于深度学习的卫星图像特征检测
Skills
语言
Python, Python 3, SQL, R, CICS, COBOL, Java, XML, JavaScript, CSS
框架
LightGBM, Apache Spark
库/ api
TensorFlow, TensorFlow深度学习库(TFLearn), Matplotlib, Scikit-learn, Pandas, NumPy, XGBoost, CatBoost, Keras, PyTorch, SciPy, Dask, LSTM, SpaCy, 自然语言工具包(NLTK), PySpark
Tools
Jupyter, GitHub, Seaborn, Plotly, Git, Spyder, Gensim, 集群, 表, JCL, 从头开始, Amazon Elastic MapReduce (EMR)
范例
数据科学,敏捷软件开发,线性编程
平台
Docker, 亚马逊网络服务(AWS), Jupyter笔记本, 谷歌云平台(GCP), WebSphere, Oracle, Tango
存储
数据管道、Google Cloud、IBM Db2、VSAM (Virtual 存储 Access Method)、MySQL
Other
数据分析, 数据分析, 数据抓取, 工程数据, 定量建模, 定量分析, 混合整数线性规划, 深度学习, 深度神经网络, 卷积神经网络(CNN), 递归神经网络(rnn), 长短期记忆(LSTM), 自然语言处理(NLP), 图像处理, 时间序列分析, 人工智能(AI), 机器学习, 建模, 统计建模, 统计方法, 统计学习, 分析, GPT, 生成预训练变压器(GPT), 统计数据, Numba, 优化, 强化学习, 深度强化学习, Dash, GloVe, 回归, 关联规则学习, 分类, 内容管理, 刮
教育
计算机科学硕士学位
贾瓦哈拉尔尼赫鲁大学-新德里,印度
数学学士学位
德里大学-德里,印度
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