Actively Looking for Data Scientist/Machine Learning Job, Available for Immediate Joining
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Highly-motivated to tackle challenges in mathematics, statistics and applied machine learning, as well as presenting and visualizing complex concepts to diverse audiences, with a curious analytical mind and a passion for all things in Artificial Intelligence and Deep Learning. Experience with mathematical and statistical Python libraries such as pandas, scikit-learn, NumPy and SciPy, Tensor Flow, Keras, and software such as MATLAB.
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Experience
1.Experience in Software Development as java developer at RELIANCE COMMUNICATION ,working in CRM team provided me whole business architecture end to end for telecom domain E.g Payment Gateways for Recharge.
2.Thoroughly assessment off Airways application used in Avaition , I Got opportunity to assess the validity off applications move to AWS CLOUD at AIRBUS, expertised in AWS Calculator for Cloud Pricing. E.g Radar, Sonar etc applications Architecture Dossier assessment.
3.Assessment off app dependency for thousand off server for Thomson Reuters applications through CloudScape scanning tool.
Also I migrated 50+ App server for reuters using AWS Cloudformation services,AWS AMS Services helps to raise RFC for all services in AWS, Closely Worked with Greater-Seattle AWS Architect Team and Sogeti USA Miiniapollise leads.Experience off Building Route53, Load Balancer,EC2,EBS Volume,IAM Role, IAM Policy ,Security Groups AWS Tranfer for SFTP and Cloudformation , worked with Migration Team.Automated tagging off EC2 and EBS Volume Resources in AWS using python Boto3 library.
4. Junior Data Scientist at Dupont-Poineer,Worked closely with onsite-team on Machine Learning model for Rice Breed Prediction based on Geographical Locations.
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Projects
1.Movie Recommendation System: All three type off recommendation systems used to predict the rating or preference that a user would give to an item. I used IMDB's weighted rating (wr) for to recommend based on movie popularity. I content based Filtering item metadata, such as genre, director, description, actors, etc. are used to suggest similar items. Collaborative Filtering matches persons with similar interests and provides recommendations based on this matching without using content metadata
2.• Time series analysis: The SARIMA model can produce spectacular results after tuning but can require many hours of time series manipulation for prediction in daily currency spends on Ads.while a simple linear regression model can be built in 10 minutes and can achieve more or less comparable results.
3.• Classification algorithm to build a model from historical data of patients, and their response to different medications. Then we use the trained decision tree to predict the class of unknown patient, or to find a proper drug for a new patient
4.• Face detection using Haar cascades a machine learning based approach where a cascade function is trained with a set of input data. Model predict the new test images based on label confidence value
5.• Gaze controlled keyboard with Python and Opencv
6.• YOLO object detection using OpenCV with Python