Principal Data Scientist

About Sand

Sand Technologies is a global Physical AI company using data and AI to make critical industries work better. We partner with governments, cities and enterprises to improve how essential systems operate across healthcare, water, energy, telecommunications and infrastructure.

Our work delivers proven real-world impact. We have built AI systems that help manage London’s water supply, supported telecom network planning across hundreds of cities, and developed digital healthcare platforms serving tens of millions of people across Africa. From intelligent command centers to AI-powered infrastructure platforms, we help organizations sense, analyze and act in complex environments.

Our people are ambitious, curious and relentlessly seek impact. Our teams work alongside clients in the field, solving hard problems and deploying solutions that influence entire industries. With colleagues across Africa, Europe, the UK and the US, we operate across the full stack - from research and development to deployment and capability building.

Our mission is simple: to harness AI to solve humanity’s most pressing challenges.

About the role

We are looking for an impact-driven Principal Data Scientist to join our growing team. Operating at the intersection of strategic vision and advanced technical execution, the Principal Data Scientist is ultimately accountable for delivering high impact solutions to our most challenging problems using data science and machine learning within our Telecommunications domain.

In this role, you will design innovative roadmaps, deliver on and oversee high-quality science implementation, and mentor our data science team, leading by example in a world-class science function. You will collaborate closely in a cross-functional team to deliver robust, scalable ML solutions, ensuring we apply the latest techniques to solve complex industry challenges and drive data-driven decision-making.

What you’ll do

• Set the Vision & Roadmap: Set an innovative vision for how we can positively impact subscriber experience, network performance, and business outcomes using data science/ML/AI. Develop strategic roadmaps to solve complex Telco problems.
• Science Implementation: Conduct and lead independent, collaborative research to develop cutting-edge models, ensuring high-quality, impactful outcomes.
• Mentor the Team: Mentorship of junior and senior data scientists within the industry, fostering a culture of continuous improvement and innovation.
• Drive Cross-Functional Alignment: Coordinate closely with software engineering, data engineering, design, and AI solution directors to integrate intelligence into scalable products and services.
• Advance the Practice: Ensure the team applies the latest techniques in data science, MLOps, decision science, predictive modeling, and AI ethics.
• Collaborate with Stakeholders: Communicate complex data science concepts and impacts to both technical and non-technical stakeholders, coordinating across a complex network of partners.
• Optimize Data Foundations: Identify how data should be improved and work closely with stakeholders to improve data quality and pipelines.
• Contribute to Leadership: Provide overall data science leadership across Sand, setting the industry team up to scale.

Who you are

• 5+ years of experience in data science and machine learning, with a proven track record of independent research, development, and putting machine learning models into production.
• M.Sc (preferably) or equivalent advanced degree in Data Science, Machine Learning, Computer Science, Statistics, or a related quantitative field.
• Prior experience in the Telco industry is highly advantageous.
• Fluency in Python with strong experience in at least one cloud provider (preferably AWS).
• Demonstrated experience mentoring and helping colleagues grow their technical and professional competencies.
• Proven ability to generate roadmaps that solve complex business problems and lead cross-functional teams at scale.
• Strong MLOps background and a deep understanding of the interplay between software/data engineering and data science, adhering to robust software engineering practices.
• Strong background in statistics and machine learning algorithms; expertise in causal data science is beneficial but not essential.

How we work

Due to the highly collaborative and internationally distributed nature of our work, successful candidates must be comfortable operating in small teams while contributing to larger, globally coordinated efforts. A strong sense of ownership, self-motivation and discipline in maintaining clear and consistent communication through virtual collaboration tools and video conferencing is essential.