Position: Data scientist / статистик
Category: Data Science
Experience: 11 years | English: upper

At Netwrix:

Prototyping advanced behavior analytics approach using R (with stack of data mining and ML libraries): UBA.

Producing core functionality of the behavior analytics reports concerning users and entities in the corporate information systems.

Developing a customer facing report prototype that operates on large scale.

Coaching colleagues on the math stat methods and ML.


At Align Technology:

Statistically significant research dive into dependancies (correlations and linear/nonlinear fits) between marketing spendings and sales. Understanding how many $$$ lead to how many units sold by channel and customer segment.

A large clinical statistical study leading to improved understanding of the new generation of clinical feature effectiveness.

A product classifier (advisor) based on customer demands with over 90% classification accuracy has been developed based on decision trees.

An explanatory model that uncovers the factors influencing the precision quality of the intraoral scanner performance by employing a machine learning method was developed for a group of engineers and algorithm developers in order to understand, forecast, and correct the errors arising in the process of the scan testing. 

A machine learning algorithm for matching similar clinical cases and finding the best fit is under development.

A churn customer predictive model that improves understanding of the customer behavior and leads to better communication between sales reps and the customers decreasing a chance of churning is developed.

Before Align Technology:

Pricing.

Creation of a reporting system from scratch, to address all vital analytical demands, including:

Forecasting sales for the period ranging from one day to several months based on statistical dynamic model.

Customer database research (ABC, segmentation, classification; employing stats and probabilities to understand and explain customer behavior).

Estimation of effectiveness of marketing efforts (ROI, ROMI).
R and stack of ML libraries
SQL
Excel
SPSS
MS Office apps
I am looking for a Data Science job with emphasis on math stats methods to help understand (any of the following): customer behaviour, marketing effectiveness, sales dynamics and influencing factors, hardware engineering systems, clinical trials, and drive the business solutions into life.

I do advanced analytics using R (preferably), including, but not limited to: A/B testing, supervised/unsupervised machine learning, data manipulating to create reports, visualizing and data story telling, preseting to technical and non-techinical audiences.

Some methods that I have recently used at work: statistical inference via t-test, chi-suare test, kolmogorv-smirnov test, binomial test, shapiro test; linear and logistic regression, regularized regression, random forest (and GBM), clusterization (hierarchical, k-means, HDBSCAN), Kohonen's SOM; neural networks.

R technology stack: stats, data.table, ggplot2, shiny, sparklyr, aws.s3, mxnet, GBM, elasticnet.

I avoid work that demands pure programming or data manipulation (which is not my goal per se), and welcome stats and ML-powered tasks.