Data Scientist

Mandatory Requirements

• Specialized education/certificates in data analysis and machine learning specializations;
• Data science work experience of 3+ years;
• Python (PEP 8);
• Ability to write code according to set quality standards;
• Knowledge of standard algorithms and data structures;
• Good command of libraries for data analysis, numerical methods;
• Ability to write time and memory optimal code;
• Algorithm complexity - code debugging and optimization;
• Test-driven development;
• Full command of OOP;
• Project architecture design experience;
• Process schedule design (airflow, cron);
• Deployment to production;
• CI/CD experience;
• Data handling: basic SQL (join, group by);
• Relational database experience;
• PySpark experience (broadcast join, etc., Spark UI);
• Be skeptical of data, check it for correctness before use;
• Ability to implement any query;
• Knowledge of window functions;
• Ability to identify suboptimal queries;
• Knowledge of the advantages and disadvantages of various database types;
• ETL pipeline development experience;
• Ability to design database structures;
• Ability to work with query plans;
• Transactions/ACID/Indexes;
• Familiar with basic probability theory and statistics;
• Familiar with A/B testing concepts and hypothesis testing;
• Ability to solve simple problems in probability theory, statistics, and logic;
• Knowledge of classical ML methods;
• Knowledge of the full cycle of A/B testing;
• Advanced command of hypothesis testing tools: multiple hypothesis testing, parametric/non-parametric methods, bootstrap;
• Study and implementation of advanced ML models: Bayesian models/PGM/VBI, RL, and others;
• Knowledge of methods for reducing variance in experiments;
• Intuition for data noise and identifying independent groups;
• Ability to analyze effects without control and test groups;
• Experience in writing articles, speaking at conferences;
• Experience working with well-described tasks;
• Experience building analytical reports with specified requirements;
• Experience presenting analytical research, validated hypotheses, etc.;
• Deep dive into product metrics: understanding core product metrics and their specifics, translating business tasks into DS/DA/DE and decomposing complex business tasks, building product proxy metrics;
• Ability to form new research directions;
• Ability to identify weaknesses/strengths in product business logic;
• Experience in consulting and working closely with product or business stakeholders;
• Experience in decomposing business goals into analysis tasks, estimating task completion times;
• Ability to assign tasks to a middle-level specialist;
• Stack: Python; SQL; data visualization (matplotlib, seaborn, plotly / BI tools); Git; PyTest; Spark 2.2+; Grafana/Airflow; Docker; Kubernetes (K8S).

Project Tasks

• Selection and construction of suitable mathematical models to solve assigned tasks;
• Selection of the optimal architecture for solving product tasks;
• Identification and elimination of product weaknesses;
• Finding product growth points;
• Synthesis of optimal mathematical models for solving product tasks;
• Close interaction with business stakeholders, consultants, and the team;
• Planning and conducting AA/A/B/A/B/N experiments;
• Maintaining the product codebase and keeping it at a stable high level;
• Assigning tasks and controlling their execution by other analysts;
• Aligning task solutions with management;
• Presenting and defending results;
• Mentoring Junior + Middle specialists.