Games Factory Talents | Connecting Passion & Talent

• Lead and develop a 4-person Growth team • Own the quality and direction of growth work across mobile gaming projects • Shape strategies across UA, ASO, monetization, retention, and analytics • Support or lead complex growth audits and strategic client engagements • Step into key accounts where senior expertise is needed • Improve team processes, reporting, and KPI visibility • Contribute to commercial decisions, scoping, and service development

• Improve and optimize AI-driven systems (e.g., make them faster, more cost-efficient, and higher quality) • Design, build, and iterate on AI agents • Fine-tune and adapt open-source language models for specific use cases • Deploy and manage models in production environments • Work on backend systems that integrate AI models into a scalable product • Collaborate closely with product and engineering to enhance core capabilities • Experiment with the latest AI tools, frameworks, and workflows • Contribute across the stack when needed in a small, high-impact team

• Own and continuously improve the full Employee Lifecycle: Attraction → Recruitment → Onboarding → Development → Retention → Exit. • Design and implement scalable HR processes aligned with company growth and business objectives. • Act as a trusted partner to leadership on people strategy, organizational design, and workforce planning. • Lead and oversee full-cycle hiring across all functions, including senior and executive-level roles. • Develop and execute effective hiring strategies, sourcing approaches, and interview frameworks. • Build and strengthen Scrambly’s employer brand and ensure an outstanding candidate experience. • Design and implement performance management systems, development frameworks, and feedback cycles. • Empower managers through clear guidelines, tools, and coaching on performance reviews, 1:1s, and people management. • Launch and own engagement and retention initiatives, including eNPS, regular 1:1s, and retention programs. • Shape, maintain, and scale Scrambly’s company culture and values across distributed teams. • Partner closely with leadership on communication, feedback culture, and employee engagement. • Lead and mentor a small HR and recruitment team, setting clear goals and development plans.

• Analyse and manage large datasets to optimise multi‑reward advertising campaigns. • Build and maintain strong professional relationships with advertisers to ensure campaign success. • Communicate with advertisers to identify new opportunities and strengthen partnerships. • Onboard new advertising partners. • Monitor the competitive landscape and develop strategies to increase revenue. • Provide regular project updates and proactively address any arising issues.

• Draft, review, and negotiate commercial and partner agreements to protect business interests • Review and manage employment and B2B contracts across multiple jurisdictions • Establish contract templates, documentation standards, and internal legal workflows • Advise leadership and cross-functional teams on day-to-day operational legal matters • Ensure compliance with local regulations across key international markets (EU, UK, US, Japan, South Korea) • Act as the primary legal partner for Product, Design, and Marketing teams • Draft, maintain, and update user-facing legal documents (Terms of Service, Privacy Policy, Cookie Policy) • Advise on data privacy, consumer protection, subscription models, refunds, cancellations, and payment flows • Collaborate with Engineering and Product to ensure privacy-by-design implementation • Serve as Data Protection Officer (or equivalent function) • Oversee compliance with GDPR, CCPA, UK GDPR, and relevant international privacy frameworks • Monitor data processing activities and maintain records of processing • Lead responses to data subject requests, data incidents, and regulatory inquiries • Develop internal privacy governance frameworks, policies, and training programs • Advise on cross-border data transfers, vendor agreements, and third-party risk assessments

• Manage a portfolio of ~7 Chinese gaming advertisers initially (expected to grow; typical account capacity is ~10–12 partnerships) • Lead end-to-end account management: kick-off → launch → ongoing optimization → growth/upsell • Build strong relationships and trust with Chinese stakeholders through confident, structured Chinese-language communication • Balance performance outcomes and quality requirements: align internal KPIs with advertiser-side quality expectations (e.g., traffic quality and performance metrics) • Create and optimize multi-reward funnels: adjust payouts, refine reward structures, and adapt funnel structures to improve results • Run recurring client calls (monthly / quarterly) and maintain consistent day-to-day communication • Collaborate with internal teams (product/ops/performance) to resolve issues quickly and improve partner outcomes • Identify upsell opportunities (additional budgets, additional titles) and drive scaling across existing partnerships

• Source, onboard, and manage an offerwall/rewarded partner portfolio (tests, launches, caps, tiering, pacing). • Build and optimize CPE goal funnels : milestone ladders, payout tiers, and performance guardrails. • Run weekly partner scorecards and decisioning: scale, pause, renegotiate, or block. • Protect quality: identify fraud/low-quality patterns (abnormal completion timing, device/source anomalies, chargebacks) and coordinate partner actions and blocks. • Own Singular as your core operating tool: integrations/config, partner setup, event mapping, attribution windows, and postbacks. • Build and maintain dashboards + reporting cadence (we have some internal tooling, but you’ll own the overall UA measurement workflow).

• Build and maintain scalable backend services and APIs using Node.js (NestJS) , writing clean, testable, maintainable code. • Design and implement complex features with a long-term view (extensibility, stability, and operability). • Improve architecture and maintainability across core systems (clear boundaries, clean interfaces, pragmatic patterns). • Own performance and reliability outcomes: monitor production, identify bottlenecks, optimize latency, and strengthen resilience. • Collaborate with DevOps to keep CI/CD, deployments, and infrastructure stable and efficient (cloud, containers, orchestration/IaC). • Partner with Product, Design, Frontend, and QA to ship high-quality releases, translating requirements into clear technical specs and trade-offs. • Raise the engineering bar via code reviews, mentoring, and standards that improve team output over time.

• Define the data science vision and roadmap (what to build, why, and in what order). • Hire, mentor, and set standards for a high-performing Data team. • Own vendor delivery and coordination (external agency) until the internal team is established. • Partner closely with product, engineering, marketing, operations, fraud, and leadership; turn business goals into measurable outcomes. • Communicate complex model tradeoffs clearly to non-technical stakeholders. • Lead the applied AI/LLM direction for product experiences and internal tooling. • Build and maintain models for personalization/recommendations, engagement prediction, churn, and LTV/forecasting. • Own the full model lifecycle: training, evaluation, deployment, monitoring, and retraining. • Work with engineering to integrate models into backend services and client experiences with performance and scalability in mind. • Design and ship LLM-enabled capabilities (e.g., semantic search, assistants, automation, content generation, decision support). • Evaluate and adapt foundation models (fine-tuning/distillation/prompting) for product and internal workflows. • Develop anomaly detection and behavioral models to identify suspicious activity. • Create scoring and classification systems for patterns like manipulation, multi-accounting, and other abuse signals. • Partner with product/ops to design thresholds, rules, validation systems, and automated risk workflows. • Track emerging fraud trends and proactively address new attack vectors. • Define requirements for scalable pipelines supporting training, inference, experimentation, and monitoring. • Implement practical MLOps standards (CI/CD for models, versioning, monitoring, automated retraining). • Raise the bar for data quality, documentation, and metadata practices. • Build infrastructure patterns for LLM workloads (vector search, embeddings pipelines, evaluation, safety checks).

• Run the full delivery lifecycle across backend, frontend, mobile, QA, and design. • Facilitate sprint planning, backlog grooming, refinement, and daily coordination to maintain pace and alignment. • Drive accurate estimation, strong task decomposition, and realistic commitments. • Monitor progress, spot risks early, and remove blockers before they affect timelines. • Keep releases predictable by tightly coordinating with engineering, QA, and product. • Create, maintain, and continuously improve delivery workflows, internal processes, and documentation standards. • Ensure task write-ups are consistently clear: requirements, acceptance criteria, and expected outcomes. • Keep the backlog structured and easy to navigate in partnership with product and engineering leads. • Set up clean communication paths and streamline handoffs between teams. • Align product priorities with engineering capacity and design readiness. • Make sure teams have the right context and inputs at each delivery stage. • Support post-release reviews, retrospectives, and ongoing process improvements.