Dmitry B.

Professional summary: Big data and analytics enthusiast, permanent learner, with about 20 years experience of data analysis and research in experimental particle physics and 12 years of data science experience in industrial settings (advertising, automotive, supply chain, energy&utility and consulting). Co-author of many software packages in experimental particle physics and industry. Leader of a few algorithmic and physics research groups and data science groups in industry. Supervised many undergraduate/PhD students, data scientists and interns in various projects. Delivery of end-to-end ML services in business companies using on-premise and cloud technologies. Primary author of more than 30 papers published in major peer-reviewed physics journals with application of machine learning algorithms in physics experiments and industrial environments: inspirehep.net/author/profile/D.V.Bandurin.1 Business website: solveum.ai A few projects have been either delivered or in progress on Upwork. Skills: – Programming in Python, R, C++, Scala, Fortran, MatLab – SQL (incl. Postgres, Redshift, Snowflake), noSQL (Mongo, Redis, BigQuery, Cassandra, Neo4j, ElasticSearch); – Big data processing using Hadoop, Databricks, Spark, Hive, Impala; – Machine learning using scikit-learn, MLLib, MLFlow, TensorFlow, Keras, PyTorch; – Distributed deep learning using Dask, Ray, Horovod; – Reinforcement learning using RLLib, Ray, COACH, OpenAI Gym; – Natural language processing [incl. Gensim/NLTK/SpaCy; GloVe/Word2Vec/FastText/BERT, etc]; – Computer vision [incl. OpenCV, OCR]; – Azure Cloud (Databricks, Delta Lake, Azure ML, Synapse Analytics, Azure IoT Hub, IoT Edge, Functions); – AWS Cloud (RDS, Amazon S3, EC2&ECR, Elastic Beanstalk, Lambda, SageMaker, etc); – Google Cloud (Vertex AI, BigQuery, DataStudio, Kubeflow, AutoML); – IBM Watson (Audio and Text modeling, transcription services); – Data visualization (Tableau, Power BI, QuickSight, Python&R libraries, e.g. Plotly, Dash, Shiny); Recommendations: see dmitrybandurin/details/recommendations/ at LinkedIn.