Agensi Pekerjaan MatchPoint Recruitment & Consulting Sdn. Bhd Hiring! Work from Home Head Of Data Analytics in , Earn up to SGD 8,000 - Ricebowl

Head of Data Analytics

Director Level | VP Level | C-Level | Senior Data Analytics Leadership

Industry: Technology / Digital Platforms / Data-Driven Operations
Employment Type: Full-Time
Work Arrangement: 100% Remote
Working Days: Monday – Friday
Working Hours: 10:00 AM – 7:00 PM (GMT+8)
Seniority: Director / VP / C-Level / Senior Leadership

About the Role

We are seeking a highly experienced and commercially driven Head of Data Analytics to lead enterprise-wide analytics strategy, strengthen data capabilities, and transform complex information into measurable business outcomes.

This is a senior leadership opportunity for an individual who combines deep technical expertise, strong business judgment, and the ability to influence strategic decisions across multiple functions and international markets.

The successful candidate will be responsible for establishing a scalable, high-performing analytics function that supports business intelligence, operational efficiency, customer behavior analysis, revenue optimization, risk management, and long-term growth.

Beyond overseeing reporting and dashboards, this role requires someone who can identify business opportunities, challenge existing assumptions, uncover performance gaps, and translate analytical findings into practical actions.

We are open to Director-level leaders, VP-level executives, C-suite professionals, and exceptionally strong senior analytics specialists who demonstrate the technical depth, strategic mindset, and ownership required to influence business outcomes at scale.

This is not a reporting-focused management position. It is a strategic role requiring an individual who understands how data drives commercial performance, operational decisions, and competitive advantage.

Key Responsibilities

1. Data Analytics Strategy & Leadership

• Develop and execute a comprehensive data analytics strategy aligned with business objectives, operational priorities, and long-term growth.
• Establish the vision, direction, and operating framework for enterprise-wide analytics capabilities.
• Translate complex business challenges into measurable analytical objectives and actionable initiatives.
• Partner with executive leadership to identify opportunities for revenue growth, profitability improvement, cost optimization, and operational efficiency.
• Establish consistent data-driven decision-making practices across business functions.
• Prioritize analytics investments based on commercial value, operational impact, scalability, and implementation feasibility.
• Develop short-, medium-, and long-term roadmaps for analytics capability development.
• Lead initiatives that transform analytics from a reactive reporting function into a proactive business advisory capability.

2. Business Intelligence & Performance Analytics

• Oversee the development, enhancement, and standardization of business intelligence frameworks, dashboards, and reporting systems.
• Establish clear performance indicators across revenue, customer acquisition, retention, conversion, operational productivity, and financial performance.
• Build analytical frameworks that accurately measure business performance across products, markets, and operational units.
• Identify emerging trends, inefficiencies, risks, and opportunities through structured data analysis.
• Ensure leadership teams receive timely, reliable, and commercially meaningful insights.
• Analyze the relationships between customer behavior, product performance, operational processes, and business outcomes.
• Develop executive-level reporting that communicates key findings, performance movements, and recommended actions.
• Challenge misleading metrics, inconsistent definitions, and analytical conclusions that lack sufficient supporting evidence.

3. Advanced Analytics, Predictive Modeling & AI

• Drive the adoption of advanced analytics techniques, including statistical modeling, forecasting, segmentation, and predictive analytics.
• Identify opportunities to leverage machine learning and artificial intelligence to improve business decisions and operational performance.
• Oversee or collaborate on the development of predictive models for customer retention, churn prevention, revenue forecasting, demand estimation, and risk identification.
• Introduce analytical approaches that move beyond historical reporting toward forecasting and proactive decision support.
• Evaluate the effectiveness of analytical models and ensure findings translate into measurable business improvements.
• Work closely with Data Science, Data Engineering, and technology teams to support model development, deployment, monitoring, and continuous improvement.
• Assess emerging analytical technologies, automation capabilities, and AI-driven tools for practical business applications.
• Ensure analytical methods remain explainable, reliable, and appropriate for their intended business use.

4. Commercial, Customer & Product Analytics

• Analyze customer acquisition, engagement, retention, churn, and lifetime value to identify opportunities for sustainable growth.
• Develop frameworks for understanding customer journeys, segmentation, and behavioral patterns.
• Evaluate product performance, feature adoption, conversion funnels, and user experience effectiveness.
• Partner with commercial, marketing, and product stakeholders to measure campaign effectiveness, customer acquisition costs, and return on investment.
• Identify opportunities to improve revenue generation, customer engagement, and product profitability.
• Lead analytical investigations into changes in commercial performance, unexpected customer behavior, or declining conversion rates.
• Support experimentation, A/B testing, cohort analysis, and performance optimization initiatives.
• Translate analytical findings into clear recommendations for business and product owners.

5. Operational Analytics & Process Optimization

• Develop analytical capabilities that improve visibility into business operations, service quality, and overall productivity.
• Analyze operational workflows to identify recurring problems, inefficiencies, performance bottlenecks, and resource allocation issues.
• Partner with Operations, Finance, Technology, Customer Service, and Risk teams to identify and resolve complex performance challenges.
• Establish metrics to assess operational effectiveness, turnaround times, service reliability, and process efficiency.
• Drive root-cause investigations into abnormal trends, performance deterioration, and recurring operational issues.
• Support capacity planning, resource forecasting, and productivity improvement through data analysis.
• Establish monitoring frameworks and early-warning indicators to detect operational risks before they escalate.
• Ensure analytical recommendations are implemented, tracked, and evaluated for measurable results.

6. Data Governance, Quality & Reliability

• Establish and maintain strong standards for data accuracy, consistency, availability, and analytical reliability.
• Define standardized business metrics, data definitions, and reporting methodologies across departments.
• Partner with Data Engineering and infrastructure teams to ensure analytics platforms can support growing business requirements.
• Identify and address data quality issues, inconsistencies, reporting discrepancies, and gaps in data visibility.
• Implement appropriate validation, monitoring, and quality assurance processes for critical analytical datasets.
• Promote responsible data usage, confidentiality, and compliance with applicable data protection requirements.
• Improve data accessibility while maintaining appropriate access controls and governance standards.
• Ensure analytical teams operate with reliable data sources and well-documented methodologies.

7. Technical & Analytical Capability Development

• Provide technical direction for advanced analytical methodologies, tools, and frameworks.
• Oversee the effective use of SQL, Python, statistical tools, and business intelligence platforms.
• Assess the suitability of technologies such as Power BI, Tableau, Looker, Snowflake, BigQuery, Redshift, and modern cloud-based analytics solutions.
• Collaborate with technical stakeholders on data modeling, transformation, integration, and analytical architecture.
• Identify opportunities to automate repetitive reporting and analytical processes.
• Introduce scalable analytical practices that reduce manual work and improve accuracy.
• Establish technical standards for analytical documentation, code quality, validation, and reproducibility.
• Ensure analytical infrastructure and processes can support increasing data volumes, business complexity, and international expansion.

8. Team Leadership & Organizational Development

• Build, lead, and develop a high-performing analytics organization comprising Data Analysts, BI Analysts, Analytics Engineers, Data Scientists, and relevant technical specialists.
• Evaluate current team capabilities and identify skill gaps, hiring priorities, and organizational development needs.
• Establish clear responsibilities, reporting structures, performance expectations, and accountability frameworks.
• Mentor senior analytical professionals and develop future technical and functional leaders.
• Foster a culture of analytical curiosity, ownership, critical thinking, and continuous improvement.
• Promote collaboration between technical specialists and non-technical stakeholders.
• Implement effective workload prioritization, resource allocation, and performance management practices.
• Establish succession planning and scalable team structures to support future expansion.

For candidates entering at a senior specialist or individual contributor level, the emphasis may be on enterprise-wide technical influence, analytical architecture, and strategic problem-solving rather than direct people management.

9. Executive Stakeholder Management & Business Partnership

• Serve as a strategic analytical advisor to senior executives and business leadership.
• Present complex findings in a clear, structured, and commercially relevant manner.
• Influence strategic decisions through evidence-based recommendations and objective analysis.
• Challenge business assumptions constructively and identify potential risks in proposed initiatives.
• Collaborate with cross-functional leadership to align analytics priorities with organizational needs.
• Establish clear ownership of analytical initiatives and ensure recommendations translate into implementation.
• Communicate analytical limitations, uncertainty, and potential business implications transparently.
• Provide regular updates on analytics performance, strategic initiatives, organizational capabilities, and improvement opportunities.

Requirements & Professional Experience

• Approximately 10–15+ years of relevant experience in Data Analytics, Business Intelligence, Data Science, Product Analytics, Business Strategy, or related functions for senior leadership consideration.
• Proven experience leading an analytics function, major analytical transformation, or enterprise-wide data initiatives.
• Demonstrated experience operating at Head of Department, Director, VP, or equivalent leadership level.
• Candidates with exceptional technical specialization and demonstrable business impact may be considered at senior specialist level.
• Strong track record of translating analytical insights into measurable commercial or operational results.
• Experience supporting complex, high-volume, data-intensive businesses.
• Proven ability to develop analytical strategies while maintaining sufficient technical involvement to challenge and guide execution.
• Experience operating across multiple business functions, products, or geographical markets is highly advantageous.

Technical Competencies

Strong practical understanding of the following areas is expected:

Data Analysis & Statistical Methods

• Advanced SQL and complex data querying.
• Python or R for analytical processing and statistical analysis.
• Statistical inference, hypothesis testing, regression analysis, and forecasting.
• Cohort analysis, segmentation, funnel analysis, and behavioral analytics.
• Experiment design, A/B testing, and causal reasoning.

Business Intelligence & Data Platforms

• Power BI, Tableau, Looker, or equivalent enterprise BI platforms.
• Cloud data warehouses such as Snowflake, BigQuery, or Amazon Redshift.
• Data modeling, transformation, and analytical data architecture.
• ETL/ELT concepts and data pipeline fundamentals.
• Dashboard architecture, KPI standardization, and performance monitoring.

Advanced Analytics & AI

• Predictive analytics and machine learning fundamentals.
• Customer propensity modeling, churn prediction, and demand forecasting.
• Model performance evaluation and practical deployment considerations.
• AI-assisted analytics, process automation, and analytical workflow optimization.

Direct hands-on proficiency across every platform is not mandatory. However, candidates must possess sufficient technical depth to evaluate solutions, identify weaknesses, and guide experienced technical professionals.

Leadership & Business Capabilities

• Strong commercial awareness and understanding of how analytics influences profitability, revenue, customer experience, and operational performance.
• Ability to connect technical findings with broader business strategies.
• Proven experience influencing senior stakeholders and driving cross-functional initiatives.
• Strong analytical reasoning, structured problem-solving, and decision-making capabilities.
• Ability to manage competing priorities and navigate ambiguity within a fast-moving environment.
• Demonstrated ownership of business outcomes rather than reporting deliverables alone.
• Excellent communication skills, particularly when presenting technical information to executive and non-technical audiences.
• Ability to establish structure, accountability, and execution discipline without unnecessary bureaucracy.

Preferred Qualifications

Candidates with the following experience will be viewed favorably:

• Previous leadership experience within large-scale digital platforms, financial technology, e-commerce, digital payments, online marketplaces, or high-transaction-volume technology businesses.
• Experience supporting international operations and working with distributed teams.
• Strong exposure to customer lifecycle analytics, monetization, growth analytics, and revenue optimization.
• Experience building an analytics department or significantly improving an existing analytical function.
• Experience modernizing data platforms, analytical frameworks, or business intelligence systems.
• Exposure to fraud analytics, transaction monitoring, payment analytics, risk modeling, or operational anomaly detection.
• Experience introducing AI or machine learning solutions that have delivered measurable operational or commercial improvements.
• Demonstrated capability to operate effectively in rapidly changing, high-growth, or highly regulated environments.
• Relevant academic qualifications in Computer Science, Statistics, Mathematics, Data Science, Engineering, Economics, or related disciplines.

What Success Looks Like

The successful candidate will be expected to deliver outcomes in the following areas.

Business Impact

• Analytics initiatives demonstrate measurable contributions to revenue, retention, conversion, cost efficiency, or operational effectiveness.
• Business leaders consistently use analytical insights to inform major decisions.
• Commercial opportunities and performance weaknesses are identified proactively.

Analytical Excellence

• Business-critical KPIs are clearly defined, standardized, and consistently measured.
• Reporting systems provide accurate, reliable, and actionable information.
• Advanced analytical capabilities improve forecasting accuracy, problem identification, and decision quality.

Operational Effectiveness

• Business and operational teams gain greater visibility into performance, risks, and inefficiencies.
• Analytical recommendations result in practical improvements rather than remaining as unimplemented reports.
• Manual reporting requirements are reduced through improved automation and scalable processes.

Organizational Leadership

• Analytics teams operate with clear responsibilities, strong technical standards, and measurable accountability.
• Cross-functional collaboration improves, with fewer delays caused by unclear requirements or inconsistent data.
• The analytics function develops sustainable capabilities that support continued expansion.

Strategic Development

• A clearly defined analytics roadmap supports both immediate business needs and longer-term objectives.
• Analytics and AI investments are prioritized according to measurable business value.
• Executive leadership has greater confidence in the organization's analytical capabilities and ability to scale.

Ideal Candidate Profile

We are particularly interested in individuals who demonstrate the following qualities:

Strategic Thinker: Understands business models, commercial drivers, and how data can support strategic decisions.

Technically Credible: Possesses sufficient analytical depth to challenge assumptions, evaluate methodologies, and guide technical teams.

Commercially Focused: Understands that analytics creates value when it improves revenue, profitability, efficiency, customer outcomes, or risk management.

Hands-On Problem Solver: Willing to investigate complex issues personally rather than relying entirely on management reports.

Builder Mentality: Comfortable establishing processes, developing teams, and improving systems rather than simply maintaining existing structures.

High Ownership: Takes responsibility for outcomes, follows through on recommendations, and measures the results of implemented solutions.

Adaptable Leader: Thrives in an environment where priorities evolve quickly and decisions must be made with incomplete information.

Strong Communicator: Can translate complicated datasets and analytical findings into straightforward business recommendations.

Career-Level Considerations

We welcome applications across several seniority levels, with the final designation determined by experience, technical capability, leadership exposure, and the scope of responsibilities.

The position is suited to experienced professionals who want to shape how data is used to drive business decisions, strengthen operational performance, and support long-term growth.

Application & Enquiries

Interested candidates who possess strong analytical capabilities, commercial awareness, and a proven track record of delivering measurable business impact are encouraged to get in touch.

We regret to inform that only shortlisted candidates will be considered for the role. Thank you:

Email: *************

Pay: From $8,000.00 per month

Benefits:

• Additional leave
• Cell phone reimbursement
• Professional development
• Work from home

Work Location: Remote