Staff Machine Learning Researcher
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Job Description
Staff Machine Learning Researcher advancing deep-learning advertising technology at RTB House. Designing production models, researching bidding solutions, analyzing A/B tests, and mentoring ML researchers.
Responsibilities:
- Identify, define, and drive research projects with company-wide scope and direct impact on core metrics
- Design and implement models, often deep neural networks, to predict Internet users’ behavior and preferences
- Develop and test new modeling approaches for issues such as bidding in first-price auctions
- Conduct and analyze A/B tests of new solutions
- Follow and analyze the latest Machine Learning research and translate it into practical improvements
- Mentor researchers and shape research practices across teams
- Own projects end to end, from conception and analysis through research, proof of concept, testing, implementation, and deployment
- Write code, train models, run experiments, and ship production ML systems
- Collaborate with stakeholders and organize work effectively
Requirements:
- 5+ years of experience as a researcher or engineer
- 2+ years of hands-on experience with Machine Learning / Data Science
- Proficiency in programming
- Sound mathematical knowledge and intuitions
- Ability to solve unclear problems by defining goals, success measures, and effective solutions
- Hands-on ML expertise, including writing code, training models, running experiments, and deploying production systems
- Results-oriented approach focused on simple, effective solutions
- Leadership skills to identify high-potential projects, create a vision, communicate with stakeholders, and execute
- End-to-end project ownership
- Effective communication and work organization
- Strong theoretical mathematics framework in statistics, probability, discrete mathematics, and combinatorics (nice to have)
- AdTech experience (a plus, not required)
Benefits:
- Attractive compensation
- Access to the latest technologies and opportunity to use them in large-scale and highly dynamic projects
- Working on extensive and rich datasets
- Work with immediate global business impact
- Mentoring opportunities and impact through mentoring less experienced colleagues
- Remote and on-site work possible
- Flexible working hours
- Knowledge-sharing culture with experienced ML professionals
- No-BS work environment
- Purpose-driven work with practical applications directly connected to business results
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