Regional AI Data Scientist Assistant Manager

Role Purpose & Context

Role Summary

The Regional AI Data Scientist Assistant Manager is here to make sure our regional business units use data and AI effectively to solve their biggest problems. You'll lead a team of data scientists, guiding them from vague business questions to deployed, impactful AI solutions. This role sits right at the intersection of technical excellence and regional business strategy, ensuring that our data science efforts aren't just academically interesting, but genuinely move the business forward. When you get this right, your region sees real, measurable improvements in things like customer retention, operational efficiency, or revenue. If you get it wrong, we're wasting valuable resources on models that don't land, and the business loses trust in what data can do. The challenge here is balancing the technical depth with the commercial realities and the constant need to develop your team. The reward? Seeing your team grow, and your models directly driving significant regional business outcomes.

Reporting Structure

• Reports to: Director of Regional Data Science
• Direct reports: Typically 3-8 Data Scientists (a mix of L2-L4)
• Matrix relationships: Principal AI Data Scientist (Regional Lead), Head of Regional Data Science, AI Solutions Manager (Regional), Senior Manager, Data Science,

Key Stakeholders

Internal:

• Regional Business Unit Heads (e.g., Head of EMEA Sales, APAC Marketing Director)
• Product Management (especially those building data products)
• Engineering Leads (for deployment and infrastructure support)
• Finance Business Partners (for ROI analysis and budget discussions)
• Global Data Science Leadership (for alignment on standards and strategy)

External:

• Key regional clients (for understanding their data needs and presenting solutions)
• External vendors (for data tools, platforms, or consulting services)
• Industry bodies and conferences (for representing our technical expertise)

Organisational Impact

Scope: This role is absolutely critical for translating global AI strategy into regional execution. You'll directly influence how data science contributes to regional P&L, driving efficiency, identifying new revenue streams, and improving customer experience. Your team's success (or failure) will have a direct, visible impact on regional business performance and our competitive standing.

Performance Metrics

Quantitative Metrics

1. Metric: Regional P&L Impact from AI Initiatives
2. Desc: The direct financial contribution (revenue generated or costs saved) attributable to models and insights deployed by your team.
3. Target: Minimum £1M annualised impact per region managed
4. Freq: Quarterly review, annual reconciliation
5. Example: Your team's churn prediction model for the DACH region led to a 15% reduction in customer churn, saving £1.2M in annualised revenue, or an optimisation model reduced operational costs by £800K.
6. Metric: Model Deployment & Operationalisation Rate
7. Desc: The percentage of developed models that successfully move from prototype to production and are actively used by the business.
8. Target: Maintain >80% deployment rate for approved projects
9. Freq: Monthly tracking, quarterly review
10. Example: Out of 10 models completed by your team in Q2, 8 were successfully deployed and integrated into business processes, hitting an 80% rate.
11. Metric: Team Productivity & Project Velocity
12. Desc: The average time it takes your team to deliver defined project milestones, or the number of high-value projects completed per quarter.
13. Target: Reduce average project cycle time by 10% year-on-year, or complete 8+ high-impact projects per quarter
14. Freq: Bi-weekly sprint reviews, quarterly summary
15. Example: Your team consistently delivers key project phases within agreed timelines, and you've seen a 12% improvement in the time from problem definition to initial model deployment compared to last year.
16. Metric: Team Retention & Development
17. Desc: The retention rate of your direct reports and their progression within the career framework.
18. Target: Achieve >90% team retention; at least one direct report promoted or taking on a lead role every 18 months
19. Freq: Annual HR review, ongoing performance discussions
20. Example: All 5 of your team members remained with us this year, and one of your L2 Data Scientists was promoted to L3 after leading a critical regional project.

Qualitative Metrics

1. Metric: Strategic Influence & Regional Business Partnership
2. Desc: How effectively you and your team are seen as trusted advisors by regional business leaders, proactively shaping their strategy with data.
3. Evidence: Regional business heads regularly seek your input on strategic planning; your team is invited to early-stage business discussions; you're seen as the 'go-to' person for data-driven insights in the region. Feedback from annual 360-degree reviews will highlight this.
4. Metric: Technical Leadership & Innovation
5. Desc: Your ability to guide your team through complex technical challenges, foster a culture of technical excellence, and introduce new, relevant methodologies.
6. Evidence: Your team consistently produces high-quality, well-documented code; you've introduced and successfully implemented new ML techniques (e.g., explainable AI, advanced time-series models); your team actively shares knowledge and best practices internally. Peer reviews and code quality metrics will show this.
7. Metric: Team Empowerment & Mentorship
8. Desc: The effectiveness of your coaching and mentorship in developing your team members' technical and professional skills.
9. Evidence: Your direct reports report high job satisfaction and feel supported in their growth; they show increasing autonomy and ability to tackle complex problems; you're actively delegating challenging work and providing constructive feedback. Employee engagement surveys and individual development plan progress will reflect this.

Primary Traits

• Trait: Strategic Visionary (with a practical streak)
• Manifestation: You can step back from the daily grind and see how a small data project fits into the bigger regional business picture. You're constantly asking 'why are we doing this?' and 'what's the actual business problem we're trying to solve?'. But you're not just a dreamer; you can then break that vision down into concrete, achievable steps for your team, making sure it actually gets built.
• Benefit: At this level, it's not enough to just build what you're told. You need to help define 'what' should be built. Regional leaders will come with vague problems, and you'll need to translate those into a data science roadmap that genuinely adds value, not just busywork. Without this, your team risks building technically impressive but commercially irrelevant solutions.
• Trait: Empathetic Leader (who isn't afraid to challenge)
• Manifestation: You genuinely care about your team's growth and well-being, providing clear guidance and support. You know when to jump in and unblock someone, and when to let them struggle a bit to learn. That said, you're also comfortable having tough conversations—giving direct feedback, challenging assumptions, and holding people accountable for their commitments. You're a coach, not just a manager.
• Benefit: Your team is your biggest asset. You'll need to build a high-performing group that trusts you, feels supported, and is constantly developing. This means being a good listener, understanding their challenges, but also pushing them to be their best. You'll be dealing with technical disagreements, project pressures, and individual development needs, so a balanced approach is key.
• Trait: Pragmatic Problem Solver (with a commercial mind)
• Manifestation: You understand that a 'perfect' model that takes a year to build and costs £100K is often less valuable than an '80% good' model that delivers £50K of value in three months. You're always thinking about the ROI of data science efforts. When faced with a complex technical challenge, you default to finding the simplest, most effective solution that meets the business need, rather than over-engineering for its own sake.
• Benefit: Resources are always finite, and business needs change quickly. You'll be making trade-offs constantly. Knowing when to stop optimising, when to use an off-the-shelf solution, and when to push for a more complex approach is crucial for delivering timely, impactful results and maintaining credibility with regional business partners. It's about delivering commercial value, not just technical elegance.

Supporting Traits

• Trait: Resilient
• Desc: You'll face project setbacks, data quality nightmares, and stakeholder disagreements. The ability to bounce back, learn from failures, and keep your team motivated is essential.
• Trait: Curious
• Desc: The AI landscape changes constantly. You're naturally interested in new techniques, tools, and how they could be applied to solve regional business problems, and you encourage this in your team.
• Trait: Transparent
• Desc: You're open about challenges, successes, and decisions with your team and stakeholders. This builds trust and helps manage expectations effectively.
• Trait: Decisive
• Desc: When your team is stuck or there are conflicting approaches, you can weigh the options, make a clear decision, and move things forward, even with incomplete information.

Primary Motivators

1. Motivator: Building and Nurturing a High-Performing Team
2. Daily: You'll spend time coaching your direct reports, helping them unpick tricky problems, and celebrating their successes. You'll get a real buzz from seeing them grow and take on more challenging work.
3. Motivator: Driving Tangible Regional Business Impact
4. Daily: You'll be constantly looking for opportunities where data and AI can genuinely improve regional operations or revenue. Seeing your team's work translate into a measurable uplift for a business unit is what gets you up in the morning.
5. Motivator: Shaping Strategic Direction and Technical Excellence
6. Daily: You'll be involved in setting the technical roadmap for data science in your region, evaluating new tools, and defining best practices. You'll enjoy the challenge of architecting solutions and ensuring your team builds things the 'right' way.

Potential Demotivators

Honestly, this job isn't for everyone. If you're looking for a purely hands-on coding role where you're always building models in isolation, you'll probably feel frustrated. You'll spend a significant chunk of your week in meetings—with your team, with regional business partners, with global leadership. You'll also deal with the messy reality of people management: performance reviews, conflict resolution, and sometimes having to deliver difficult news. You won't always be the one writing the code, and you'll often be reviewing others' work or unblocking them rather than doing the 'fun' part yourself. Expect to be the person who has to explain why a model isn't a magic bullet or why a 'quick fix' isn't actually quick.

Common Frustrations

1. The constant tension between regional specific needs and global standardisation efforts.
2. Spending more time on people management and stakeholder alignment than on deep technical work.
3. Dealing with legacy data infrastructure or data quality issues that constantly derail project timelines.
4. The pressure to deliver 'AI' solutions even when simpler statistical methods would be more appropriate and faster.
5. Explaining statistical concepts and model limitations to non-technical leaders who just want a definitive answer.

What Role Doesn't Offer

1. A purely individual contributor role with minimal management responsibilities.
2. A predictable, routine work schedule with no urgent, high-priority shifts.
3. The luxury of building models without considering their commercial viability or deployment challenges.
4. A 'clean room' environment where data is always perfect and infrastructure is always readily available.

ADHD Positives

1. The varied nature of managing multiple projects and a team can be stimulating, preventing boredom.
2. Strong ability to hyperfocus on critical problems when a team member is blocked or a project is at risk, leading to rapid resolution.
3. Often brings a fresh, innovative perspective to problem-solving and team strategy, challenging conventional thinking.

ADHD Challenges and Accommodations

1. Managing a diverse team and multiple regional stakeholders requires strong organisational skills and attention to detail, which can be challenging. We can use tools like Asana or Trello for project tracking and provide dedicated admin support for scheduling.
2. The constant context switching between technical deep-dives, people management, and strategic meetings might be overwhelming. We encourage time-blocking for focused work and offer flexible meeting schedules where possible.
3. Delegation can be tricky; there might be a tendency to take on too much. We'll work with you to build robust delegation habits and provide coaching on effective task distribution.

Dyslexia Positives

1. Often excels in big-picture strategic thinking and identifying patterns that others miss, crucial for setting regional data science direction.
2. Strong verbal communication and storytelling skills, which are vital for presenting complex AI concepts to regional business leaders.
3. Excellent problem-solving abilities, especially when visualising complex systems or data flows.

Dyslexia Challenges and Accommodations

1. Reading and reviewing detailed technical documentation, code reviews, or lengthy reports can be time-consuming. We encourage the use of text-to-speech software, provide templates for structured documentation, and prioritise verbal communication for initial feedback.
2. Writing clear, concise emails and reports to stakeholders can be challenging. We offer proofreading support, grammar checking tools, and encourage bullet points for key messages.
3. Organising complex information for presentations might require extra effort. We provide access to presentation templates and tools that help structure content visually.

Autism Positives

1. Exceptional ability to deep-dive into complex technical architectures and data models, ensuring robust and scalable solutions for the region.
2. Strong adherence to logical processes and technical standards, which is vital for maintaining code quality and MLOps best practices across the team.
3. Direct and honest communication style, which can be highly effective in technical discussions and providing clear feedback to the team.

Autism Challenges and Accommodations

1. Navigating complex social dynamics with diverse regional stakeholders and managing team conflicts might be challenging. We offer coaching on interpersonal communication, conflict resolution, and provide clear frameworks for stakeholder engagement.
2. Unplanned changes in project scope or team priorities can be unsettling. We aim for clear communication about changes well in advance and provide structured planning tools.
3. Participating in large, unstructured meetings can be overwhelming. We encourage pre-reading materials, provide agendas, and ensure opportunities for input via written channels or smaller group discussions.

Key Responsibilities

Experience Levels Responsibilities

1. Level: Principal AI Data Scientist (Regional Lead) / Manager
2. Responsibilities: Set the technical vision and strategic direction for data science within your assigned region, ensuring alignment with global objectives and local business needs.
3. Build, mentor, and manage a high-performing team of 3-8 data scientists (L2-L4), providing regular coaching, performance feedback, and career development opportunities.
4. Own the regional data science project portfolio, from ideation and prioritisation with business partners to successful deployment and impact measurement.
5. Architect and oversee the development of complex AI/ML solutions, ensuring they are robust, scalable, and adhere to our MLOps and coding standards.
6. Act as the primary technical expert and trusted advisor for regional business unit heads, translating complex data science concepts into clear, actionable business insights.
7. Manage budgets for regional data science initiatives, including tool procurement, external services, and resource allocation, typically ranging from £500K to £2M annually.
8. Drive continuous improvement in data science methodologies, tools, and processes across your team, fostering a culture of innovation and learning.
9. Represent the regional data science capability in global forums, sharing best practices and contributing to the overall company-wide AI strategy.
10. Supervision: You'll operate with a high degree of autonomy, reporting to the Director of Regional Data Science with quarterly objective setting and strategic alignment discussions. Day-to-day, you're expected to be self-directed, managing your team and projects independently.
11. Decision: You'll have full authority for technical decisions within your regional domain, including model architecture, tool selection (within approved frameworks), and project methodologies. You'll own hiring decisions for your direct reports and manage budgets up to £1M without direct approval, consulting your Director on anything above that or for significant strategic shifts. Organisational design within your team is also your call, though larger departmental changes would require Director input.
12. Success: Your success will be measured by the tangible business impact your team delivers (e.g., £1M+ P&L contribution), the successful deployment rate of your team's models (>80%), the retention and growth of your direct reports, and your ability to effectively influence regional business strategy with data-driven insights.

Decision-Making Authority

• Type: Project Prioritisation & Resource Allocation
• Entry: Follows assigned tasks and priorities.
• Mid: Prioritises own tasks within project scope, escalates conflicts.
• Senior: Prioritises workstreams within a project, makes recommendations to lead.
• Type: Technical Architecture & Tool Selection
• Entry: Uses specified tools and follows architectural patterns.
• Mid: Selects appropriate models/algorithms for defined problems, suggests minor tool improvements.
• Senior: Designs end-to-end solutions for workstreams, recommends new tools/frameworks within a project.
• Type: Budget & Vendor Management
• Entry: No budget authority.
• Mid: No budget authority.
• Senior: Recommends software/data purchases up to £5K for specific projects.
• Type: Hiring & Performance Management
• Entry: No hiring or performance management authority.
• Mid: No hiring or performance management authority.
• Senior: Provides input on junior hires, mentors new team members.

Competency Requirements

Foundation Skills (Transferable)

Beyond the technical wizardry, a manager needs a solid set of 'human' skills to truly excel. These are the abilities that help you lead a team, navigate complex organisational landscapes, and communicate effectively with everyone from junior analysts to regional VPs.

• Category: Leadership & Team Development
• Skills: Mentoring and coaching data scientists at various experience levels.
• Delegating tasks effectively and fostering ownership within the team.
• Providing constructive feedback and managing performance.
• Building a positive, collaborative, and inclusive team culture.
• Motivating and inspiring a team through challenging projects.
• Category: Strategic & Commercial Acumen
• Skills: Translating regional business challenges into data science opportunities.
• Understanding the commercial impact and ROI of AI initiatives.
• Prioritising projects based on business value and strategic alignment.
• Developing and communicating a clear data science roadmap for the region.
• Navigating organisational politics and gaining buy-in for initiatives.
• Category: Communication & Influence
• Skills: Presenting complex technical concepts clearly to non-technical regional executives.
• Negotiating with stakeholders to manage expectations and project scope.
• Facilitating productive discussions and driving consensus across diverse groups.
• Writing clear, concise, and impactful reports and proposals.
• Active listening and asking probing questions to uncover underlying needs.
• Category: Problem Solving & Decision Making
• Skills: Deconstructing ambiguous regional business problems into solvable data science challenges.
• Making sound technical and strategic decisions under pressure and with incomplete information.
• Identifying and mitigating risks associated with AI projects (e.g., data quality, model bias).
• Troubleshooting complex technical issues and unblocking team members.
• Applying a pragmatic approach to problem-solving, balancing perfection with delivery.

Functional Skills (Role-Specific Technical)

This role demands a deep technical foundation combined with the ability to think at an architectural and strategic level. You're expected to be an expert in the data science lifecycle, capable of guiding your team through any challenge.

Technical Competencies

• Skill: Advanced MLOps Strategy & Implementation
• Desc: You'll define and oversee the MLOps strategy for your region, ensuring models are deployed, monitored, and maintained effectively. This means understanding CI/CD for ML, model versioning, feature stores, and drift detection.
• Level: Expert
• Skill: Enterprise Data Architecture & Governance
• Desc: You'll understand how data flows across the organisation, from source systems to the data warehouse and into models. You'll contribute to data governance policies, ensuring data quality, security, and accessibility for your team.
• Level: Advanced
• Skill: Complex Model Validation & Explainability (XAI)
• Desc: Beyond simple accuracy, you'll guide your team on rigorous model validation, including fairness, robustness, and interpretability. You'll need to explain complex model behaviours to regional stakeholders using techniques like SHAP or LIME.
• Level: Expert
• Skill: Data Storytelling for Executive Audiences
• Desc: You'll be able to distil complex analytical findings and model outputs into compelling narratives that resonate with regional business leaders, clearly articulating the 'so what' and the commercial implications.
• Level: Expert
• Skill: Advanced Feature Engineering & Selection
• Desc: You'll guide your team in identifying and creating the most impactful features from raw data, understanding the trade-offs between feature complexity, model performance, and interpretability.
• Level: Expert

Digital Tools

• Tool: Python (TensorFlow, PyTorch, Scikit-learn, Pandas, NumPy)
• Level: Strategic
• Usage: Setting coding standards, evaluating new libraries, architecting complex ML pipelines, and providing expert-level code reviews for your team.
• Tool: SQL (PostgreSQL, BigQuery, Snowflake, Redshift)
• Level: Architectural
• Usage: Designing optimal database schemas, data warehousing strategies, making platform decisions, and optimising complex queries for large-scale regional data sets.
• Tool: Cloud Services (AWS - SageMaker, EC2, S3, Lambda, RDS, IAM)
• Level: Strategic
• Usage: Designing and architecting enterprise-wide cloud data platforms, managing budgets, security, and governance across AWS services for regional deployments.
• Tool: MLOps & Version Control (Git, GitHub/GitLab, Jenkins, Kubeflow, MLflow)
• Level: Strategic
• Usage: Defining the organisation's MLOps strategy, selecting and implementing platforms for enterprise-wide model management, and ensuring robust CI/CD pipelines for regional models.
• Tool: Data Visualization (Tableau, Power BI, Streamlit, Plotly/Dash)
• Level: Strategic
• Usage: Governing the enterprise visualisation platform, defining how key regional business metrics are reported to the executive level, and ensuring data products are impactful.
• Tool: Data Warehousing (Snowflake, Databricks)
• Level: Architectural
• Usage: Leading platform selection and enterprise implementation for regional data warehousing, setting governance, access control, and cost management policies.

Industry Knowledge

• Area: Regional Market Dynamics & Business Drivers
• Desc: Deep understanding of the specific economic, competitive, and customer landscape within your assigned region, and how these factors impact business performance and AI strategy.
• Area: Ethical AI & Responsible AI Frameworks
• Desc: Knowledge of best practices for ensuring fairness, transparency, and accountability in AI systems, particularly concerning regional regulatory requirements and societal impacts.
• Area: Data Privacy Regulations (e.g., GDPR, CCPA, local equivalents)
• Desc: Comprehensive understanding of relevant data privacy laws and how they impact data collection, storage, processing, and model deployment in your region.
• Area: AI/ML Vendor Landscape & Partner Ecosystem
• Desc: Familiarity with leading AI/ML platforms, tools, and service providers, and the ability to evaluate and select appropriate partners for regional initiatives.

Regulatory Compliance Regulations

• Reg: General Data Protection Regulation (GDPR)
• Usage: Ensuring all regional data collection, storage, processing, and model training activities comply with GDPR, particularly concerning personal data and cross-border transfers. Guiding the team on data anonymisation and consent management.
• Reg: Local Data Protection Acts (e.g., UK Data Protection Act, specific EU member state laws)
• Usage: Understanding and applying specific regional nuances of data protection laws that might go beyond GDPR, ensuring local compliance for all data science projects.
• Reg: AI Act (EU) / Emerging AI Regulations
• Usage: Staying abreast of and preparing for upcoming AI regulations, assessing the risk classification of your team's AI systems, and ensuring future compliance, especially for high-risk applications.

Essential Prerequisites

• Proven experience (8+ years) as a Senior or Lead Data Scientist, with a strong track record of delivering complex, impactful AI/ML projects end-to-end.
• Demonstrable experience in leading small teams or mentoring junior data scientists, with a passion for developing others.
• Deep expertise in at least one major cloud platform (e.g., AWS, Azure, GCP) for building and deploying ML solutions.
• A strong portfolio of projects where you've translated ambiguous business problems into clear data science initiatives and delivered measurable results.
• Excellent communication and stakeholder management skills, with a history of influencing senior leaders and cross-functional teams.
• A solid understanding of MLOps principles and experience in operationalising machine learning models in production environments.

Career Pathway Context

You won't just 'fall' into this role. You'll have spent years honing your technical craft, proving your ability to deliver, and starting to take on leadership responsibilities. This role is the natural next step for a Staff or Lead Data Scientist who's ready to take on people management and strategic ownership, moving beyond individual contributions to driving team and regional impact.

Qualifications & Credentials

Emerging Foundation Skills

• Skill: AI Governance & Ethical Framework Leadership
• Why: With increasing regulatory scrutiny (like the EU's AI Act) and growing public awareness of AI's societal impact, leading with a strong ethical compass and robust governance frameworks isn't optional—it's essential. Regional variations in ethical considerations will also become more prominent.
• Concepts: [{'concept_name': 'Fairness & Bias Detection', 'description': 'Understanding how to identify and mitigate algorithmic bias in models, particularly for sensitive regional demographics.'}, {'concept_name': 'Transparency & Explainability (XAI)', 'description': 'Leading the team in building models whose decisions can be understood and explained to both technical and non-technical regional stakeholders and regulators.'}, {'concept_name': 'Accountability & Human Oversight', 'description': 'Defining processes for human intervention and accountability when AI systems make critical decisions, especially in high-risk regional applications.'}, {'concept_name': 'Data Provenance & Lineage', 'description': 'Ensuring clear documentation of data sources, transformations, and usage to support auditability and compliance.'}]
• Prepare: This quarter: Review the latest draft of the EU AI Act and identify potential impacts on your regional projects.
• Next 3 months: Lead a team workshop on ethical AI principles and integrate bias detection into your model validation pipeline.
• Next 6 months: Engage with our Legal and Compliance teams to develop a regional AI governance framework.
• Within 12 months: Implement tools for automated data lineage tracking for all production models.
• QuickWin: Start by adding a 'Bias & Fairness' section to all your team's model documentation templates. It's a small step, but it gets everyone thinking.

Advancing Technical Skills

• Skill: Generative AI & Large Language Model (LLM) Integration Strategy
• Why: Generative AI is transforming how we interact with data and build applications. As a manager, you'll need to understand how to strategically apply LLMs for regional use cases, manage their lifecycle, and address their unique challenges (e.g., hallucination, prompt injection).
• Concepts: [{'concept_name': 'Prompt Engineering & Optimisation', 'description': 'Understanding how to effectively design prompts for various LLM tasks and optimise them for performance and cost.'}, {'concept_name': 'Retrieval Augmented Generation (RAG)', 'description': 'Leading the integration of LLMs with proprietary regional data sources to improve accuracy and relevance.'}, {'concept_name': 'Fine-tuning & Custom Model Development', 'description': 'Evaluating when to fine-tune open-source LLMs versus using pre-trained models for specific regional needs.'}, {'concept_name': 'LLM Evaluation & Monitoring', 'description': 'Developing robust methods to evaluate LLM outputs and monitor their performance and safety in production.'}]
• Prepare: This month: Experiment with different LLM APIs (e.g., OpenAI, Anthropic) to understand their capabilities and limitations for regional tasks.
• Next 3 months: Identify one high-impact regional use case for LLMs (e.g., automated report generation, customer support summarisation) and pilot a solution with your team.
• Next 6 months: Develop a strategy for integrating RAG architectures with our internal knowledge bases.
• Within 12 months: Establish guidelines and best practices for responsible LLM deployment within your region.
• QuickWin: Encourage your team to use LLMs for boilerplate code generation, documentation, and summarising research papers immediately. It's low risk and high reward.

Education Requirements

• Level: Minimum
• Req: A Bachelor's degree in Computer Science, Statistics, Mathematics, Engineering, or a related quantitative field
• Alts: Equivalent practical experience (e.g., 4+ years in a highly technical data role beyond the 12-16 years required for this level) and a strong portfolio demonstrating advanced data science and leadership capabilities will also be considered.
• Level: Preferred
• Req: A Master's or PhD in a quantitative field (e.g., Data Science, Machine Learning, AI, Operations Research)
• Alts: Relevant industry certifications (e.g., AWS Certified Machine Learning – Specialty, Google Professional Data Engineer) combined with extensive practical experience.

Experience Requirements

You'll need roughly 12-16 years of progressive experience in data science, with a significant portion (at least 3-5 years) in a Senior or Lead Data Scientist role. Crucially, you'll need demonstrable experience in managing or formally mentoring a team of data scientists, including performance management, career development, and project oversight. We're looking for someone who has a proven track record of architecting and delivering complex, high-impact AI/ML solutions in a commercial setting, ideally with exposure to regional business challenges. Experience managing budgets and influencing senior stakeholders is also essential.

Preferred Certifications

• Cert: AWS Certified Machine Learning – Specialty
• Prod: Amazon Web Services (AWS)
• Usage: Demonstrates advanced skills in designing, implementing, and maintaining ML solutions on the AWS platform, which is critical for cloud-native AI strategies.
• Cert: Google Professional Data Engineer / Machine Learning Engineer
• Prod: Google Cloud
• Usage: Shows expertise in building and managing data processing systems and ML models on Google Cloud, relevant for multi-cloud or GCP-focused environments.
• Cert: Certified Analytics Professional (CAP)
• Prod: INFORMS
• Usage: Validates your ability to apply analytics to complex business problems, manage projects, and communicate results effectively, which is key for a managerial role.

Recommended Activities

• Regularly attend industry conferences (e.g., ODSC, Strata Data & AI) to stay current on trends and network with peers.
• Participate in leadership training programmes focused on coaching, conflict resolution, and strategic communication.
• Contribute to open-source data science projects or publish articles/blog posts on relevant topics.
• Actively seek out opportunities to mentor junior colleagues and participate in internal knowledge-sharing sessions.
• Complete online courses or certifications in advanced MLOps, ethical AI, or specific cloud platform specialisations.

Career Progression Pathways

Entry Paths to This Role

• Path: From Senior/Staff Data Scientist (IC Track)
• Time: 3-5 years as a Senior/Staff Data Scientist
• Path: From Data Science Consultant (External)
• Time: 4-6 years in a senior consulting role focused on AI/ML strategy and implementation
• Path: From Engineering Manager (with strong Data/ML background)
• Time: 3-5 years as an Engineering Manager with a focus on data platforms or ML infrastructure

Career Progression From This Role

• Pathway: Director of Regional Data Science (L6)
• Time: 3-5 years in this Manager role

Long Term Vision Potential Roles

• Title: Chief Data & AI Officer (L7)
• Time: 10-15+ years from this role
• Title: VP of Engineering / CTO (with AI specialisation)
• Time: 8-12+ years from this role
• Title: Head of Product (AI Products)
• Time: 7-10+ years from this role

How Zavmo Delivers This Role's Development

DISCOVER Phase: Skills Gap Analysis

Zavmo maps your current competencies against all requirements in this job description through conversational assessment. We evaluate your foundation skills (communication, strategic thinking), functional skills (CRM expertise, negotiation), and readiness for career progression.

Output: Personalised skills gap heat map showing strengths and priorities, estimated time to competency, neurodiversity accommodations.

DISCUSS Phase: Personalised Learning Pathway

Based on your DISCOVER results, Zavmo creates a personalised learning plan prioritised by impact: foundation skills first, then functional skills. We adapt to your learning style, pace, and neurodiversity needs (ADHD, dyslexia, autism).

Output: Week-by-week schedule, each module linked to specific job responsibilities, checkpoints and milestones.

DELIVER Phase: Conversational Learning

Learn through conversation, not boring modules. Zavmo uses 10 conversation types (Socratic dialogue, role-play, coaching, case studies) to build competence. Practice difficult QBR presentations, negotiate tough renewals, and handle churn conversations in a safe AI environment before facing real clients.

Example: "For 'Stakeholder Mapping', Zavmo will guide you through analysing a complex enterprise account, identifying key decision-makers, and building an engagement strategy."

DEMONSTRATE Phase: Competency Assessment

Zavmo automatically builds your evidence portfolio as you learn. Every conversation, practice scenario, and application example is captured and mapped to NOS performance criteria. When ready, your portfolio supports OFQUAL qualification claims and demonstrates competence to employers.

Output: Competency matrix, evidence portfolio (downloadable), qualification readiness, career progression score.