• Working with a team of data scientists and software engineers to leverage massive amounts of structured and unstructured data (image, video, audio, text, metadata) stored on in-house data clusters by using state of the art algorithms and analysis tools• Developing software in a fast-paced Agile setting in a big data environment working with different tools including Python, R, SQL, QGIS, PostGIS, Gremlin, Spark, Hadoop, REST API, ELK Stack, Docker, Portainer, Huawei Cloud, and Amazon… Show more • Working with a team of data scientists and software engineers to leverage massive amounts of structured and unstructured data (image, video, audio, text, metadata) stored on in-house data clusters by using state of the art algorithms and analysis tools• Developing software in a fast-paced Agile setting in a big data environment working with different tools including Python, R, SQL, QGIS, PostGIS, Gremlin, Spark, Hadoop, REST API, ELK Stack, Docker, Portainer, Huawei Cloud, and Amazon S3• Analyzed and mined text data and developed software to enrich unstructured data with valuable attributes and built a pipeline for automatic real-time data mining using Huawei cloud• Used Logstash to ingest data to Elasticsearch indices, created visualizations, and built live dashboards on Kibana, and presented demos to key stakeholders• Trained computer vision deep learning models on GPU DGX servers, built REST API services on top of these models, and deployed these services using NVIDIA-Docker on Portainer• Communicated previously unseen potential use cases of existing data to product owners• Worked as a technical committee member to evaluate and give feedback on AI product development partnerships with two multinational tech companies. Involvement included testing APIs, participating in video conference calls and in-person meetings and communicating feedback to key stakeholders from both sides• Maintained proper documentation on Confluence and Jira, and code version control on GitLab• Assisted in recruiting data scientists and conducted peer-interviews to evaluate candidates• Assisted in onboarding and training of new data scientist hires Show less

Using statistical and machine learning techniques and Busineess intellgance methods.Using the naive bays, decision trees and k-NN to discover paterns and knowledge from a large amount of data.Data analysis, data mining and text miningBuilding predictive models and time series analysis methods.Working with large, complex data sets.Solving difficult, non-routine analysis problems.Run various analytical models such as Dynamic, live pricing models and Linear programming… Show more Using statistical and machine learning techniques and Busineess intellgance methods.Using the naive bays, decision trees and k-NN to discover paterns and knowledge from a large amount of data.Data analysis, data mining and text miningBuilding predictive models and time series analysis methods.Working with large, complex data sets.Solving difficult, non-routine analysis problems.Run various analytical models such as Dynamic, live pricing models and Linear programming models.Articulating business questions and using mathematical techniques to get the answers using available data.Translating analysis results into business recommendations.Applying advanced analytical methods as needed.Conducting end-to-end analysis that includes data gathering and requirements specification, processing, analysis, ongoing deliverable, and presentations.Developing comprehensive understanding of data structures and metrics.Making business recommendations (e.g. cost-benefit, forecasting, and experiment analysis). Show less