Data Science Lead

The BETER product company is looking for a Data Science Lead to develop and implement a technical strategy for the development of a system for sports analytics. If you're passionate about leveraging data to drive innovation and make a meaningful impact, then this position is for you.

Important for the position:

- Bachelor's or Master's degree in Computer Science, Statistics, Mathematics, or related field;

- 5 years of proven experience in a data science role, with a track record of delivering impactful solutions in a fast-paced environment.

- Be a business person, have a sense of responsibility for the entrusted area and treat it as your brainchild, be demanding of the team, but fair, set goals, monitor their implementation, control the workload of people, and systematically improve the level of skills in the team;

- Experience in managing a team: organizing work, motivating, maintaining discipline, delegating and monitoring execution, exchanging feedback, and leadership;

- Knowledge of probability theory, ability to build applications based on this theory;

- Hands-on experience with Python (numpy, pandas, matplotlib, etc);

- Deep understanding of machine learning algorithms, statistical modeling techniques, and experimental design principles;

- Strong problem-solving skills and a passion for tackling complex analytical challenges;

- Loyalty to DevOps practices and experience in their application: automation of development and quality assurance tools, monitoring, tracing, and debugging;

- Adhere to the opinion that without code review it is impossible to work and convey this message to the masses :);

- Striving to improve existing solutions and develop new solutions, searching for growth points;

- A desire to share knowledge with engineers and be able to convey it.

It will be a plus:

- Hands-on experience developing in C#;

- Experience in digging into the insides of frameworks for self-improvement;

- A constant search for answers to the questions “why” and “how to do it better”, but at the same time critical thinking to understand when it is rational to do it “better”;

- Understanding of data structures and algorithms, understanding how O(1) differs from O(n);

- Experience in cloud environments in general and AWS in particular.

What do you have to do?

- Lead and mentor a team of data scientists, providing guidance and support to ensure successful project execution and professional development;

- Planning and distribution of work in a team, monitoring implementation;

- Drive the development and implementation of cutting-edge machine learning algorithms and statistical models to extract actionable insights from large datasets;

- Collaborate with business stakeholders to understand key objectives and translate them into analytical solutions that deliver measurable business value;

- Design and execute experiments to test hypotheses to build prediction algorithms;

- Design of services on the balance of the team in collaboration with the architect and business analysts;

- Implementing and maintaining good software development practices, such as coding conventions, and code review;

- Assistance in troubleshooting, and training on how to prevent them in the future;

- Conducting knowledge tests, developing tests for self-control and for hiring needs;

- Participation in interviews of team candidates, assessing the level of knowledge and expertise;

- Providing regular feedback to team members;

- Conducting team meetings on a regular basis;

- Approval of vacations, sick leave, time off, overtime in the team, and correction of current plans.

What and how do teams work?

- A highly loaded system based on .NET 8, which receives changes in real-time and recalculates sports analytics;

- Working in a microservice architecture, with a messaging system;

- Ability to choose technologies to effectively achieve results;

- Experienced and strong specialists in teams who are always ready to share knowledge;

- The code is packaged in Docker and lives in AWS EKS;

- Kafka for real-time messaging;

- MongoDB, PostgreSQL for data storage and processing;

- Victoria Metrics for collecting metrics and monitoring;

- ElasticSearch for logs;

- Teams are cross-functional and focused on their services;

- Minimum bureaucracy, the ability to easily communicate with all levels of management;

- We strive to make decisions quickly, without months of discussions, if a person has analyzed the risks and alternatives and understands the essence of his proposal.