Deputy CDO
S
Social Discovery GroupSocial discovery
Location: Worldwide Our international team of 1000+ professionals and digital nomads works all over the world. Our teams of digital nomads work remotely from Cyprus, Malta, the USA, Armenia, Georgia, Kazakhstan, Montenegro, Poland, Latvia, Serbia, Spain, Portugal, UAE, Israel, Turkey, Thailand, Indonesia, Japan, Hong Kong, Australia and many other locations.Full-TimeVp
Salary not disclosed
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Job Details
- Languages
- Strong English and Russian communication skills, both written and verbal.
- Experience
- 6–10+ years in data, analytics, or ML domains, including 3–5+ years in leadership roles.
- Required Skills
- Machine LearningProduct AnalyticsRisk ManagementA/B testing
Requirements
- 6–10+ years in data, analytics, or ML domains, including 3–5+ years in leadership roles.
- Proven track record delivering cross-functional projects involving ML, data platforms, experimentation, and analytics.
- Experience in product companies (not only consulting environments).
- Strong understanding of product analytics, A/B testing, unit economics, and key business metrics.
- Solid understanding of the ML lifecycle — from ideation to production.
- Familiarity with data platforms, data quality management, SLA processes, and self-service BI/analytics.
- Strong execution skills: project decomposition, planning, prioritization, and delivery management.
- Ability to coordinate cross-functional teams across ML, backend, product, and analytics functions.
- Experience managing risks, dependencies, escalations, and multiple parallel initiatives.
- High technical literacy with the ability to dive deep into data and ML topics, understand system constraints, and work closely with engineering and data teams.
- Strong English and Russian communication skills, both written and verbal.
Responsibilities
- Own execution of cross-functional strategic initiatives across ML, experimentation, and data platform domains.
- Lead 2–3 high-priority initiatives during the first 3 months: define scope, align stakeholders, establish ownership, and drive delivery to measurable outcomes.
- Ensure predictable execution through clear planning, coordination, and delivery management.
- Unblock stalled or ambiguous projects by clarifying ownership, resolving dependencies, escalating risks early, and driving initiatives through completion.
- Build and operate an effective execution framework: monthly business reviews, quarterly planning, retrospectives, project reviews, and escalation processes.
- Establish a single source of truth for project status, risks, commitments, and delivery tracking.
- Improve the experimentation ecosystem by increasing decision-making speed, standardizing evaluation and rollout processes, and reducing operational chaos/manual work.
- Accelerate ML delivery cycles by reducing time from idea → experiment → production and coordinating dependencies across ML, backend, analytics, and product teams.
- Ensure ML initiatives deliver measurable business impact, not only technical outcomes.
- Strengthen functional leads (Product Analytics, Data Engineering, ML, UX) through prioritization support, execution discipline, feedback, and managerial coaching.
- Increase ownership, accountability, and cross-functional collaboration across teams.
- Own operational excellence of the function, including data freshness, uptime, SLA adherence, and delivery predictability.
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