Machine Learning Engineer, Data Scientist

alexander senov

data scientist

About

Full-time employee and a PhD student. Passionate about both machine learning theory and its application aimed to solve real-world problems. Think that the word ”science” in ”data science” is excess.

Experience

09/2011 - nowadays Lead Data Scientist E-Contenta LLC Developing multi-purpose SaaS recommender system product Main responsibilities:

• Designing product architecture (python code, deployment, CI).

• Product maintaince and development.

• Performing data acquisition, preprocessing and analysis.

• Experiments with various recommendation algorithms.

• Leading a small team of data scientists.

Technologies: Mongo, Docker, Ansible, Kafka, Tornado. Programming languages: Python.

10/2011 - 09/2012 Software Engineer in Test Synqera LLC Building an offline retail recommender system based on top of Apache Hadoop stack (hadoop, sqoop, hive, impala, hbase, spark). Main responsibilities:

• Developing ETL process for batch recommendations delivery.

• Transforming business requirements and client requests into functional requirements.

• Setting up and testing hypothesis, predictive models building & assessment, data wrangling.

• Data exploration, visualization and reports generation. Technologies: Hadoop, Hive, Impala, Spark, CDH, MsSQL. Programming languages: Java, Scala, Python, R.

10/2011 - 09/2012 Software Engineer in Test Yandex LLC Automated testing of Yandex.Market web service (Java, JUnit, Sele- nium).

Education

2012 - 2017 (exp.) Ph.D., Applied Math Saint Petersburg State University Randomized Methods in Optimization

and Estimation Problems

2012 - 2013 Further Education, Data Analysis Computer Science Center Machine Learning, Computer Science

2007 - 2012 Specialist, Applied Math Saint Petersburg State University Specialization in Statistical Modelling

contants

alexander.senov

@gmail.com

Linkedin

Github

languages

russian native

english upper

intermediate

programming

Sufficient: Python

(data science stack)

Java SE, R, bash;

Superficial: Scala,

C++, javascript (d3.js),

Octave, SQL.

technologies

(in random order)

Ansible, Docker, Git,

Ludgi, Mongo, Kafka,

Hadoop, Spark, Hive,

HBase, Impala,

SQLite, MsSQL,

OpenCV, Caffe,

Tensorflow.

ds fields

Recommender

systems;

NLP (sentiment

analysis, topic

modelling, document

classification, QA

system);

Computer vision

(OCR, image

classification, object

detection);

Customer analysis

(customer

segmentation, churn

prediction, LTV,

scoring).

Online courses

2015 Scalable Machine Learning edX

edX Honor Code Certificate

2015 Introduction to Big Data with Apache Spark edX edX Honor Code Certificate

2015 Statistical Learning Stanford Online

Honor Code Certificate

2014 Mining Massive Datasets Coursera

Statement of Accomplishment

2013 Principles of Reactive Programming Coursera

Statement of Accomplishment

2013 Introduction to Data Science Coursera

Statement of Accomplishment with Distinction

2012 Machine Learning Coursera

Statement of Accomplishment with Distinction

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