OpenAI07/25/2026
Специалист по Data Science (вывод на рынок и ускорение внедрения корпоративного ИИ)
Salary not specified
MARKET
15,900 ₽median for this role
Data Scientist · 115 jobs with disclosed pay
5,000half of the offers: 12,796–20,50043,793
The employer didn't disclose pay — compare with the market yourself.
Полная занятостьУдалёнка
Responsibilities
- 01Define a trusted measurement framework for knowledge-worker adoption, including identity, eligible populations, activation, retained usage, penetration, workflow depth, feature adoption, and monetization
- 02Map the knowledge-worker journey from initial exposure through first successful task, repeated workflows, multi-surface usage, and durable adoption
- 03Identify which personas, functions, use cases, product capabilities, and account conditions are associated with deep and retained usage
- 04Design and evaluate experiments and quasi-experiments across onboarding, enablement, workflow templates, connectors, pilots, customer deployment support, and product launches
- 05Combine behavioral data with customer and field evidence, then translate the findings into crisp recommendations for Product, GTM, Finance, and executive audiences
- 06Operationalize successful work through durable datasets, scorecards, recurring business narratives, and decision cadences while partnering with Analytics Engineering and product teams to improve instrumentation and data quality
Requirements
- 01Significant experience in data science, product analytics, growth analytics, economics, statistics, or a related quantitative field
- 02Strong hands-on ability in SQL and Python, including experience working with large and imperfect behavioral datasets
- 03Experience defining activation, retention, engagement, funnel, or product-adoption metrics, with strong knowledge of experimentation, causal inference, cohort analysis, and segmentation
- 04Ability to translate ambiguous business questions into structured analyses, independently define the analytical direction, and bring senior stakeholders along through clear tradeoff framing
- 05Strong written and verbal communication skills, including a demonstrated ability to influence senior technical and non-technical stakeholders
- 06Enjoy creating clarity from problems that do not yet have stable definitions, clean datasets, or a settled playbook
- 07Move comfortably between SQL, Python, metric design, experimentation, customer evidence, strategy, and executive communication
- 08Think in terms of user journeys and behavioral mechanisms—not only dashboards and aggregate metrics
- 09Can distinguish product usage from durable customer value and simulated value from realized business outcomes
- 10Proactively align stakeholders, document decisions, and surface data-quality risks before they affect important decisions
- 11Are comfortable challenging an attractive narrative when the evidence does not support it