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Wuqi Weber
BASIC
United States, Covington
Applied Data Scientist / Analysis Wuqi Weber, Engineering & Science
A certified Applied Data Scientist with experiences focusing on statistics analytics and machine learning. Familiar with statistics modeling, feature engineering and commonly used machine learning algorisms. My last project was working on an anomaly detection project for Microsoft Digital Stores using Microsoft cognitive service with some python public packages and Microsoft internal Bias Test library. • Experienced descriptive statistics and advanced analytics in Cosmos big data environment for MSCOM Digital Store. • Experienced causal impact modeling for camping incrementality analysis. • Experienced reinforcement learning modeling using Markov Decision Process for MSCOM online customer behavior analysis and action recommendations. • Experienced MS stores KPIs anomaly detection and root cause analysis conduct with forecasting model to reduce the false alerts. Writing python class library for internal use case. • Experienced Azure VM auto scaling schedule time series forecasting, classifications for MS Azure Subscription Automation Team. • Participated in data science professional activities, o Cortana Intelligence Competition Project 2016 – Decoding Brain Signals Achieved top 10th in public leader boarder - accuracy 81.875% in public test dataset. Achieved top 12th in private leader boarder - accuracy 80% in private test dataset. Program Language: R Platform: R Studio, MS Azure Machine Learning Web service. Algorism: LASSO most like hood features selection and logistic regression o Kaggle Competition project - Classify Handwritten Digits Using MNIST data Program Language: R “mxnet” deep learning model, accuracy: 99.429% Program Language: Python “Tensorflow” deep learning model, accuracy: 99% • Experienced simulation science tool development using modem web technologies and PowerSim SDK.
Engineering & Science
Data Mining
Data Science
Deep Learning
Machine Learning (ML)
Statistical Analysis
70 $
Mohd Musharaf Shaikh
BASIC
India, Navi Mumbai
Data Analyst, Business Analyst, Research Analyst, Business Intelligence Mohd Musharaf Shaikh, Engineering & Science
Enthusiastic professional Data Analyst & Business Analyst with 2+ year of experience in Data analytics seeking to work in an organization where I can deliver best of my abilities & provide useful insights with machine learning algorithms & A.I to clients from their data. Innovation is key to success & I work on projects using A.I methodologies & create innovative products & services. With regard to my ability to meet the specific requirements : • EDA : Analyse and transform complex data sets to optimize dashboard flexibility and performance • Data Analytics: Provide thoughtful leadership and influence executive level decision making through deep analytical insights, embedding data & analytics • A.I Model : Certificate in Data Science has provided training in the use of skills includes writing Algorithm, data processing, data warehousing, statistical analysis, NLP, KPIs and written communication/presentation. My teachers and employers have commended me for my high level of interpersonal skills and naturally engaging personality. My motivations include learning new things and the challenge of meeting key objectives. I believe that my motivation, commitment and pre-existing skills will allow me to fit into your work environment and immediately start supporting the needs of your organisation. I would appreciate the opportunity to meet with you to discuss my application at an interview. I have enclosed a copy of my resume for your consideration. I can be contacted at all times on the details provided above. Thanking you in advance for your time, Mohd Musharaf.A.Shaikh
Engineering & Science
Data Mining
Machine Learning (ML)
Data Science
AI (Artificial Intelligence) HW/SW
Deep Learning
10 $
Rajat Patyal
BASIC
Data Scientist Rajat Patyal, Engineering & Science
Rajat Patyal Engineering & Commercial Services 01/01/2010 – Till Date Designation- Data Scientist Profile: Experienced And Dedicated Data Scientist With Several Years Of Experience Using Predictive Analysis And Running Machine Learning Supervised And Unsupervised Algorithms On Data Derived From Formats Like Exl, Csv & Xml. I Am Adept In Deriving Inferences From Data Procured From Different Sources And Using Keras, Python, Numpy And Pandas For Presentation And Visualizing Data. I Have Also Cleared Google Analytics, Adwords And IBM Data Science Professional Capstone Project. ◦ Using Ibm Watson Studio And Aws For Storing Data And Jupyter Notebook Running Machine Learning & Deep Learning Algorithms. Projects - Natural Language Learning Projects Using Keras . Aug/01/2018 – Till Date Project 1 Using Keras Sequential Model Collating Data Of 10 Million Movie Reviews And Categorizing Them As Positive Or Negative. Converted Text Reviews Into 5000 Binary Features Indicating 1000 Of The Most Frequently Used Words. Used 5 Million Observation Each With 1000 Features To Classify Positive Or Negative Reviews. Paramaetrs Defined - • Feature And Target Vector Of Training Data. • Epoch Parameters • One Hot Encoded Feature Matrix And Target Matrix. • Add Fully Connected Layer To Relu Activation And Softmax Activation • Compile Neural Networks • Train Neural Networks Project 2 Converting Reuters Newswire Into 46 Labeled Topics. Parameters Defined - • Feature And Target Vector Of Training Data. • Epoch Parameters • One Hot Encoded Target Data To Ascertain Target Matrix With Classification Of 46 Classes. • Increased The Number Of Units In Each Of The Hidden Layers to Help Neural Network Represent The Complex Relation Among The 46 Classes. • Used An Output Layer With 46 Units Containing Softmax Activation Function Which Returned An Array Of 46 Values Summing To 1 Which Represented The Probability Of Each Observation Being A Member Of The 46 Classes. • Added Dropout To Both Hidden And Input Layer And Used Cross Entropy In The Loss Function. Invoices And Bill Reading Automated . Apr/01/2018 – Till Date Role – Business Intelligence Analyst & Data Engineer Pre-Processing - Used Rescaling, Binarization, Noise Removal, Thresholding, Dilation And Deskewing To Make The Invoice Image Better. Model Building - • Used Pytesseract, You Can Get The Bounding Box Information For Ocr Results With The Confidence Scores. • Extracted The Characters Of The Invoices And Forwarded It To The Database Team. Data Science Project For Sprint Mobil Usa . Apr/01/2018 – Till Date Role – Business Intelligence Analyst & Data Engineer Discovery Phase Of The Project- To Evaluate The Different Factors That May Lead To Customer Churn And Build A Model Based On Logistic Regression(Python)And Selecting The Factors Which Made The Most Impact. For Classification. Data Preparation Included Extracting Transforming And Loading Data From Different Sources And In House Data Warehousing System. • Preprocessed By Replacing Imputed Values With KNN. • Outlier Detection in the Data. • Imbalanced classes data preparation. • Categorical Values Were Processed By Using One Hot Encoding. • Applied Standard Scaler To Reduce Variance In Data . • Used Principal Component Analysis For Best Feature Selection And Visualized The Outcome With Regression Tree. Model Planning- Choose Between Linear Regression And Logistic Regression Multinomial. After Thorough Evaluation Chose Experimentation Chose Logistic Regression Decision Tree. Model Building- Trained And Tested Data, Etc And Chose The Best Possible Possible Features For The Outcome Using PCA. • Used Stochastic Average Gradient Solver As The Data Set Was Large. • Hyper Parameter Tuning With Gridsearchcv. • Evaluated Performance Of The Model With Cross Validation. Result- • F Value Of .98 On The Trained And Tested Model Showed That The Model Was Extremely Effective. Communicate Results - Communicated Results To The Stakeholders And Team For Forming A Viable Customer Retention Policy. Data Science Project For Autotitre.Com Jan/01/2016 – Mar/01/2017 Role – Business Intelligence Analyst & Data Engineer Discovery Phase Of The Project- To Evaluate The Different Factors That May Lead To The Pricing Of A Used Car And Choosing The Best Feature To Accurately Predict The Price Of The Car Regression. Data Prepration Included Extracting Transforming And Loading Data From Different Sources And Websites As Well As Procuring Data From Oltp. Olap And Website Api’s. Model Planning- Choose Between Linear Regression And Ridge Regresssion And Using Anova Or Manova. After Careful Deliberation Ridge Regression Was Selected. • Dropped The Na, Nan, Null Values. Model Building- Trained And Tested Multiple Factors Like Mileage, Age, Make, Fuel Type, Color, Model, Body, Etc And Chose The Best Possible Possible Features For The Outcome. Reset The Alpha For The Polynomial Equation As The For Fitting The Curve. Result- • MSE Of .10 On The Trained And Tested Model Showed That The Model Was Extremely Effective. Communicate Results - Forwarded The Outcome Of The Regression Analysis To The Front End Team So That They Could Update Their Dashboard With The Predicted Results . Data Science Project For Chevron Texaco. Jan/01/2017 – Till Date Role – Business Intelligence Analyst & Data Engineer Discovery Phase Of The Project- To Forecast The Prices Of Gasoline Prices For A Particular Day For Usa And Canada. Data Preparation Included Extracting Transforming And Loading Data From Different Sources And Websites As Well As Procuring Data From Oltp. Olap And Website Api’s Model Planning- Selected Time Series Arima Model After Validation Of The Normality Function And Availability Of Historical Time Series Data. Model Building- Forecasted Prices Of Gasoline Based On 12 Years Historical Price. Result- • Precision Of .90 On The Trained And Tested Model Showed That The Model Was Extremely Effective. Communicate Results - Forwarded The Forecasted Prices To The Respective Teams. Data Science Project For Ibm. Jan/01/2019 – Nov/28/2020 Role – Business Intelligence Analyst & Data Engineer Discovery Phase Of The Project The Different Factors Which Cause The Greatest Number Of Complaints For New York Municipal Corporation. Model Preparation - Data Stored On Ibm, And Aws Elastic Search To Store Data Used Numpy And Scipy And Pandas To Evaluate The Data And Presented The Conclusion Model Building- Used Pearson Correlation To Group Problems And Come To 2 Reoccurring Problems Which Accounted To Maximum Number Of Incident Reporting. Predicted The Future Outcome Result- • MSE OF .1 On The Trained And Tested Model Showed That The Model Was Extremely Effective. Communicate Results - Forwarded The Forecast Result To The Respective Team. Date Of Birth: 20 February 1980 Nationality: Indian Education Qualifications / Certification: • B.Com (Hons ) 2001 Delhi University. • Advance Diploma In Software (Niit) 2000 Covering Java, Sql, Linux, Nosql. • Capstone Project Ibm Machine Learning And Data Science (2020). • Data Analysis Using Python • Tensorflow For Deep Learning • Data Visualization Using Python • Sql Access For Hadoop • Gaiq
Engineering & Science
Deep Learning
Data Mining
Data Science
50 $
George Lomia
BASIC
Software Engineer George Lomia, Engineering & Science
Giorgi Lomia EDUCATION Berea College, Berea, KY August 2017 - May 2021 Double Major: B.A. Computer Science, B.A. Mathematics. GPA: 3.8/4 Relevant Courses: Data Analytics, Machine Learning, Deep Learning, Software Engineering, Data Structures, Programming Languages, Calculus 2, Fundamentals of Math, Linear Algebra, Computational Neuroscience. SKILLS Programming Languages: Python, C++, Java, JavaScript, R, Visual Basic, HTML, CSS, SQL. Artificial Intelligence: TensorFlow, Keras, OpenCV, Sckit, Pytorch, Sklearn, Pandas. Operating Systems: Linux, Google Cloud, Windows, macOS. Languages: Russian, Georgian, Polish. EXPERIENCE Teacher Assistant for Software Design and Data Structures, Berea College: August 2018-Now • Analyzed and graded +30,000 lines of code, to provide feedback and suggest improvements to students. • Initiated +50 labs and projects for students, to encourage solving Python and C++ problems. • Facilitated 130 students to explore programming and data structures and increased average grade by 34%. Software Engineer Intern, Kinetic Vision: June 2020- August 2020 • Developed a full semantic segmentation pipeline for a dataset of over 100,000 images with 54 classes. • Designed and implemented a computer vision pipeline for one of the largest retailers in the world. • Integrated an IoT sensor system, using over 1,000 hours of footage. • Collaborated with a diverse team to improve retail stocking process speed by 8%. Android Development Tech Lead, Codepath: December 2019- August 2020 • Led, coordinated, and organized 15 Android developers, creating a variety of applications. • Developed and designed 10+ Android applications, as well as helped students design over 30 applications. • Increased the largest pipeline of high-performing underrepresented engineers in tech via Codepath. • Taught Facebook-designed, for credit, 12-week Android development course on campus with 25+ students. Software Engineer Intern, System Corp: June 2019- August 2019 • Wrote an image segmentation and classification algorithm, using Python and C++ to diagnose 50 skin diseases. • Launched a full diagnosis pipeline from image reception to inference on 22 hospital servers. • Reduced diagnosis time from 2 hours to 2 seconds per patient and improved diagnostic accuracy by 4%. • Collaborated with and learned from a dedicated team of 12 data scientists and software developers, to solve diverse problems. • Performed 356 hours of applied research in machine learning, contributing to the medical research community. Software Engineer Intern, Kashmir World Foundation: April 2019- June 2019 • Developed an artificial intelligence system, in C++ and Python, to classify and detect animals in camera trap image feed, to help save snow leopards and other 12 endangered species. • Wrote a research paper on 412 hours of applied, deep learning, research conducted. • Cut down animal image classification process conducted by preservation workers from 2 weeks to 5 minutes, by implementing a full image processing pipeline. • Overcame challenges of occlusion, camouflage, and localization for a data set containing +100,000 images. PROJECTS Deep Learner: (Python, Tensorflow, Keras): • Designed an interactive, easy to use GUI, allowing users to import, visualize, and inspect data, in 24 hours. • Devised a system enabling users to construct, train, and run neural networks that are 15+ layers deep without writing any code. QR & Barcode Detector (Python, OpenCV): • Implemented a Computer Vision system using OpenCV, detecting Barcodes and QR codes within 0.5 seconds. • Streamlined barcode and QR code lookup and identification of 154,212,372 items and websites. WatermarkRemover (Python, Tensorflow, Keras): • Designed a watermark remover system, to identify the weaknesses of modern copyright protection. • Created a network to denoise and clear watermarks from a large variety of images. InstaParse (Java, Android Studio, XML, Heroku): • Designed an app similar to Instagram in functionality. • Users can view the last 20 posts submitted to InstaParse.
Engineering & Science
Deep Learning
Engineering
Machine Learning (ML)
50 $
Surekha Ramireddy
BASIC
India, Anantapur
Data Analyst with Tableau Developer // Data Scientist Surekha Ramireddy, Engineering & Science
I'm a Full-Stack Data scientist with business/Computer Science background with a specialized focus on the Data science and visualization data; I have worked in the last two years developing Data products, Explorations, visualizations, and modeling ML as a Lead Data Scientist with the focus on business and marketing to improve the results of my clients that will increase the profit; I can help you to answer questions like: Will my client stop buying from me? (churn) What's the probability of a fraudulent transaction? (fraud prediction) What will be the demand for the next quarter? (demand prediction) How many different groups I have in my clients? (customer segmentation) Who is my client and what's the best group that gives me more profit? (segmented prediction) What's the chance of default? (default prediction) Will my employee left my company? (attrition) RFM, CLV, and many other marketing tasks with machine learning And much more... Ever focusing on your business; I'm an awarded data scientist with a great business understanding that had helped more than 30 companies to improve their profits through Data Platforms, Dashboards, NLP techniques, Clustering, Data understand & Exploration, Machine learning Explainability and much more. I have experience working in: - Exploratory Data Analysis - Data Visualizations - Machine Learning - Deep Learning - Statistical Hypothesis test - Data Modelling - Feature Engineering - Natural Language Processing (NLP) - Clustering / Dimension Reduction - Forecasting - Web Development - Business/Financial domain Main skills: - Languages/Tools: Python, R, SQL - Frameworks/Libs: Pandas, Dash, Plotly, TensorFlow, Pytorch, Keras, Numpy, Scikit-Learn, PySpark, NLTK, Spacy, OpenCV, LightGBM, XGBoost, SciPy, Flask - Analysis: statistic, calculus, classification/clustering, machine learning, deep learning - Others: Git, Docker, Linux - AWS Elastic Beanstalk, AWS s3, AWS EC2, AWS Cognito - And a lot about architecture and infrastructure. - Data Project Manager - Data Products Specialist _______________________________________________
Engineering & Science
Data Science
Machine Learning (ML)
Deep Learning
25 $
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