Deputy CDO

We are looking for Deputy CDO.

Your main tasks will be:

• 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.

We expect from you:

• 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.