Head of Geospatial Data Science

Gravitas Recruitment Group (Global) Ltd; Singapore; Vor Ort; SGD 180.000 - 240.000; Vollzeit

Zusammenfassung

Gravitas Recruitment Group (Global) Ltd seeks an experienced data science leader to set technical direction and guide advanced analytics across large-scale data challenges. The role combines strategic ownership, technical leadership, people development, and practical delivery to translate ambiguous problems into measurable outcomes.

You will advance analytical methods, improve data product reliability, and ensure models deliver sustained value in real-world environments, balancing long-term

Qualifikationen

• Bachelor's or master's in a quantitative/computational field.
• Extensive experience leading data science teams and ML/AI programs.
• Strong knowledge of ML methods and model evaluation.
• Solid understanding of graph structures and related algorithms.
• Experience applying DS techniques to spatial, location, or movement problems.
• Strong Python and SQL skills with ML libraries.

Aufgaben

• Set data science strategy and priorities aligned with product goals.
• Lead design and deployment of ML solutions for large-scale data problems.
• Oversee team development and collaboration with product/engineering.
• Ensure robust model evaluation, monitoring, and governance.
• Translate complex data problems into structured delivery plans.

Kenntnisse

Python

SQL

ML

Leadership

Strategy

Ausbildung

Quant degree

Tools

Graph algorithms

Spark

Cloud platforms

Jobbeschreibung

We are seeking an experienced data science leader to establish technical direction, guide the development of advanced analytical capabilities, and lead the application of machine learning to complex, large-scale data challenges.

This role combines strategic ownership, technical leadership, people development, and practical delivery. You will shape the direction of data science initiatives, oversee the development of intelligent data-driven solutions, and work with colleagues across technical and business functions to translate ambiguous problems into measurable outcomes.

You will be responsible for advancing analytical methods, improving the reliability and usefulness of data products, and ensuring that models and algorithms deliver sustained value in real-world environments. The position requires someone who can move comfortably between long-term planning, technical problem-solving, product collaboration, and the development of a strong engineering-oriented data science practice.

Key areas of responsibility

Strategy and technical direction

• Develop and execute a data science strategy that supports broader product objectives and evolving business needs.
• Set priorities for research, experimentation, model development, and the adoption of new analytical capabilities.
• Establish sound technical approaches to complex problems involving interconnected data, spatial relationships, movement patterns, and changing real-world conditions.
• Evaluate emerging methods in artificial intelligence, machine learning, statistical analysis, and optimisation, identifying opportunities for practical application.
• Promote robust analytical standards, reproducible experimentation, and evidence-based decision-making.

Advanced modelling and solution development

• Lead the design and development of machine learning solutions that improve data accuracy, completeness, timeliness, consistency, and decision-making.
• Guide the application of statistical modelling, predictive methods, deep learning, optimisation techniques, and graph-based algorithms to complex problems.
• Oversee the analysis of large, heterogeneous datasets, including information relating to physical locations, movement, visual observations, and behavioural patterns.
• Address challenges involving the relationships between geographic features, the interpretation of location information, the identification of relevant places, network structures, and the assessment of data quality.
• Ensure that analytical approaches are appropriate to the problem, technically robust, scalable, and capable of producing useful outcomes.
• Assess model performance and explain results in ways that support sound technical and business decisions.

Delivery and operational excellence

• Own the analytical development process from initial problem definition and exploratory analysis through experimentation, validation, deployment, and ongoing refinement.
• Work with software, platform, and data engineering colleagues to integrate models into reliable production services and workflows.
• Establish appropriate practices for model evaluation, monitoring, testing, version control, and continuous improvement.
• Guide decisions about scalability, operational reliability, maintainability, and the effective use of computational resources.
• Encourage the use of clear performance measures and feedback mechanisms to assess the impact of deployed solutions.
• Translate complex or uncertain requirements into structured delivery plans and practical, measurable outcomes.

Leadership and collaboration

• Lead, mentor, and develop a team of data science professionals, supporting technical growth, career progression, and sustained performance.
• Build team capability through recruitment, skills development, coaching, and effective allocation of work.
• Create an environment that encourages intellectual curiosity, constructive challenge, experimentation, and shared accountability.
• Collaborate with product and technical leaders to align analytical priorities with wider delivery objectives.
• Communicate complex methods, trade-offs, findings, and recommendations to audiences with varying levels of technical expertise.
• Influence decisions across functional boundaries and build agreement on approaches to challenging, data-intensive problems.

Essential qualifications and experience

• A bachelor's or master's degree in a quantitative or computational discipline, such as mathematics, statistics, physics, economics, computer science, or a related field.
• Extensive professional experience in data science, machine learning, artificial intelligence, or a closely related technical discipline, including a substantial track record of leading and developing data science teams.
• Strong theoretical and practical knowledge of machine learning methods, including the ability to assess competing approaches, challenge assumptions, and guide technical decisions.
• A solid understanding of graph structures, graph algorithms, and their application to complex real-world systems.
• Demonstrated experience applying data science techniques to spatial, location-related, network-based, or movement-related problems.
• Strong Python programming skills and practical experience with established machine learning libraries, together with working knowledge of SQL.
• Experience analysing large, complex datasets and developing scalable approaches to problems with incomplete information or evolving requirements.
• An understanding of model interpretability and explainable machine learning, with the ability to communicate findings and their implications clearly.
• Experience working with spatial or location-related information, such as positioning data, geographic features, interconnected networks, or records associated with physical places.
• Strong leadership, communication, and stakeholder-management skills, with a demonstrated ability to collaborate effectively across technical and business functions.

Additional experience that would be beneficial

• Experience applying machine learning to images, video, or other forms of visual data, including computer vision techniques.
• Familiarity with graph-based machine learning approaches, including graph neural networks.
• Experience using major cloud computing environments and distributed data-processing or workflow-orchestration technologies.
• Knowledge of production software development practices, including version control, automated testing, continuous integration and delivery, and model deployment workflows.
• A track record of developing, deploying, and maintaining machine learning systems that operate reliably at substantial scale.

What success looks like

Success in this role means establishing a clear and sustainable technical direction for data science, developing a capable and effective team, and delivering analytical solutions that address complex real-world challenges.

You will help improve the quality and reliability of data-driven products, strengthen the transition from experimentation to production, and build effective partnerships across technical disciplines. Over time, your work will contribute to a mature data science capability that combines scientific rigour, practical engineering, sound judgement, and demonstrable product impact.