Analytics Head | Location

Role Overview

The Head of Analytics will lead the organisation's analytics vision, strategy, and execution. This role involves leveraging data-driven insights to support decision-making, improve operational efficiency, and drive business growth. The ideal candidate will have extensive experience in analytics leadership, strong business acumen, and the ability to work across multiple functions.

Responsibilities
Strategic Leadership

• Develop and implement a company-wide analytics strategy aligned with business goals; deliver a documented strategy and 12-month roadmap within the first 3–6 months and update quarterly.
• Define KPIs, measurement frameworks, and success metrics for data-driven initiatives; establish baselines within the first quarter and set measurable targets (e.g., reporting cadence, adoption, accuracy) with progress reported monthly to the executive team.
• Prioritise analytics initiatives by business impact and feasibility, ensuring the top-priority projects demonstrate measurable outcomes (e.g., revenue uplift, cost reduction, process efficiency) within agreed timeframes.

Data & Analytics Management

• Lead end-to-end analytics initiatives: data collection, cleaning, modelling, and visualisation, ensuring delivery against agreed SLAs (e.g., pipeline availability, timeliness of datasets).
• Oversee the development of dashboards, predictive models, and analytical reports for business units; establish delivery timelines (typical sprint length, review cadence) and measurable success criteria such as dashboard adoption rates and decision-impact metrics.
• Drive the adoption of analytics across departments to embed data-based decision-making in daily operations; set targets for adoption (for example, a measurable increase in analytics-driven decisions or stakeholder satisfaction within 6–12 months) and track against them.
• Maintain data quality and governance standards; implement monitoring and quality KPIs (e.g., data accuracy, completeness, and latency) and reduce critical data issues by defined percentages within the first year.

Team Leadership

• Manage, mentor, and grow a high-performing analytics team, including data scientists, analysts, and BI specialists; establish clear OKRs for the team and conduct regular (quarterly) performance and development reviews.
• Create hiring and skills-development plans to fill capability gaps within defined timelines (e.g., key hires within first 6–12 months) and measure success via ramp time and project delivery metrics.
• Foster a culture of continuous learning, innovation, and collaboration; set measurable goals for knowledge-sharing activities (workshops, brown-bags, certification completion) each quarter.

Cross-Functional Collaboration

• Partner with business leaders in Marketing, Sales, Product, Finance, and Operations to identify analytics needs and deliver actionable insights; agree on project goals and SLAs for delivery (e.g., time-to-insight targets) and measure stakeholder satisfaction post-delivery.
• Work closely with IT/Data Engineering teams to ensure data availability, accuracy, and governance; jointly establish and monitor data platform SLAs (e.g., uptime, latency) and resolve issues within agreed timelines.
• Communicate results and recommendations clearly to non-technical stakeholders, with a cadence of monthly/quarterly executive updates and documented business impact for major initiatives.

Innovation & Best Practices

• Stay updated with the latest analytics, AI, and ML trends, tools, and technologies; evaluate and recommend at least one new tool or technique per year that can demonstrably improve capabilities or efficiency.
• Evaluate and implement advanced analytics techniques to improve forecasting, customer understanding, and operational efficiency; define success measures for pilots (e.g., uplift in forecasting accuracy or conversion rates) and move successful pilots to production within defined timelines.
• Establish and enforce best practices for reproducible analytics, model validation, and documentation; set targets for compliance and audit readiness and report improvement on these metrics regularly.

Job Requirements

Education

• Bachelor's/Master's degree in Statistics, Mathematics, Data Science, Computer Science, Economics, or related field.
• MBA or equivalent is a plus.

Experience

• 10–15 years of experience in analytics, with at least 5 years in a leadership role.
• Proven track record of leading analytics functions across industries.
• Hands-on experience with analytics tools such as SQL, Python/R, Power BI/Tableau, and big data platforms.

Skills & Competencies

• Strong understanding of statistical modelling, data mining, and machine learning techniques.
• Excellent communication and storytelling skills to present insights to senior stakeholders.
• Ability to translate complex data findings into clear business strategies.
• Strong project management skills and the ability to handle multiple priorities.

Skills: leadership,analytics,power bi,sql,tableau