Full-time. Remote possible.
Mandatory Requirements
- Specialized education/certificates in data analysis and machine learning specializations;
- At least 3 years of experience in data science;
- Python (PEP 8);
- Ability to write code according to specified quality standards;
- Knowledge of standard algorithms and data structures;
- Proficient in libraries for data analysis, numerical methods;
- Ability to write code optimal in time and memory;
- Algorithm complexity - debugging and code optimization;
- Test-driven development;
- Full proficiency in OOP;
- Experience in project architecture design;
- Designing process schedules (airflow, cron);
- Deploying projects to production;
- Experience with CI/CD;
- Working with data: basic SQL (join, group by);
- Experience with relational databases;
- Experience with PySpark (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 different database types;
- Experience in developing ETL pipelines;
- 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 conducting A/B tests;
- Advanced proficiency in hypothesis testing: 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;
- Have an 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 with well-defined tasks;
- Experience in building analytical reports with specified requirements;
- Experience in presenting analytical research, verified hypotheses, etc.;
- Deep dive into product metrics: understanding key product metrics and their specifics, translating business tasks into DS/DA/DE and decomposing complex business tasks, building proxy product metrics;
- Ability to form new research directions;
- Ability to identify weak/strong sides of product business logic;
- Experience in consulting and working closely with product managers or business stakeholders;
- Experience in decomposing business goals into analytical tasks, estimating task completion times;
- Ability to assign tasks to a mid-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 for solving assigned tasks;
- Selection of optimal architecture for solving product tasks;
- Identifying product weaknesses and their elimination;
- Finding product growth opportunities;
- Synthesis of optimal mathematical models for solving product tasks;
- Close interaction with business stakeholders, consultants, and the team;
- Planning and conducting AA/A/ABN experiments;
- Maintaining the product codebase and keeping it at a consistently high level;
- Assigning tasks and monitoring their execution by other analysts;
- Approving task solutions with management;
- Presenting and defending results;
- Mentoring Junior + Middle specialists.
