Nikita Seleznev

Nikita Seleznev is a proficient data scientist with a profound background in research and technology product development, excelling in amalgamating math, statistics, computer science, and domain knowledge to derive useful insights.

With a notable track record, Nikita has contributed as a lecturer, session chair, and member of technical committees for prominent scientific and professional organizations, receiving multiple distinguished speaker and industry accolades.

Specializing in Artificial Intelligence, Machine Learning, and Data Mining, Nikita's expertise spans across various domains including Statistics, Algorithms & Complexity. He is well-versed in a multitude of machine learning algorithms such as Generalized Linear Model, Naive Bayes, SVM, Decision Tree, Random Forests, Boosting, and Artificial Neural Networks.

Nikita Seleznev is adept in Natural Language Processing and LSTM Networks, along with possessing strong communication skills and storytelling abilities to convey complex data insights effectively.

Proficient in Python libraries like Numpy, Pandas, Matplotlib, Seaborn, Scikit-learn, Statsmodels, and Tensorflow, Nikita also brings expertise in R programming with tools like ggplot2, dplyr, randomForest, e1071, and nnet, coupled with proficiency in SQL for database management.

Having pursued a Doctor of Philosophy (PhD) at Technische Universiteit Delft, Nikita Seleznev has held key roles at Schlumberger, formerly serving as the Principal Scientist: Project Manager for Applied Math & Data Analytics and as Research Group Manager, showcasing his leadership and expertise.

In summary, Nikita Seleznev's multifaceted skill set, extensive experience in data science and research, combined with his leadership roles in reputed organizations, establish him as a distinguished professional in the field.