Position: Strong Junior Data Analyst
Company: airSlate
Experience required: 1y

**Responsibilities:**
— Support and validation of statistics on A/B experiments.
— Comprehensive analysis of user behavior in the product and using behavior patterns to predict future events (churn, upgrade etc.).
— Cross-product user journey analysis.
— B2B lead scoring.
— In-depth analysis of reasons for cancellations.
— AI — bots for business process automation.

**What we offer:**
— Health insurance.
— Budget for courses and attending conferences.
— Monthly performance bonuses.
— Each employee (after 1.5 years in the company) receives his/her option contract.
— Open communication with top management at regular Q&A sessions.
— Free lunches with delivery to the office.
— Paid English lessons.
— Gym with yoga, jiu-jitsu, TRX classes.
— Full-time massage therapist in the office.
— Paid vacation time (16 working days during the 1st year at airSlate, 24 days after the 1st year).
— 5 paid sick days per year, no medical certificate.
— Paid sick leave on presentation of medical certificate.
— Book Box — to order books in English, Ukrainian or Russian through the sharing economy.
— airSlate Delivery — every two weeks every team member gets home delivery of useful gifts from the company
— airSlate Care — we donate 1% of our time and resources to support and develop charitable projects.
— airSlate Kids Club — we organize educational online and offline events for employees’ children.

**Required skills:**
— Experience working with databases from 1 year.
— Writing moderately complex SELECT queries.
— Aggregation functions + grouping.
— Understanding the difference between different types of JOIN.
— Experience with Python/R to work with datasets and related data analysis packages.
— Strong knowledge of descriptive statistics, understanding of statistical hypothesis testing methods.
— Strong knowledge of probability theory and understanding of basic probability distributions and their applicability to real-world problems.
— Knowledge of classical linear models and clustering methods and their optimization.
— Understanding of exploratory data analysis approaches, visualization, principles of dealing with incomplete data, unbalanced datasets.
— Understanding of basic marketing and product metrics and methods of their calculations.

**Will be a plus:**
— Knowledge and experience in ML.
— Understanding of query optimization principles.
— Understanding of Bayesian statistics and MCMC methods.
— Knowledge of bash, Git/Github, Docker.
— Experience with GoogleAnalytics (Reporting API, RealTime API), GoogleAds.
— Basic knowledge of NLP methods (frequency analysis, bag of words, tf-idf, Ngrams).