Job Overview
As Lead Data Science & DataOps Engineer, you will lead our Data Science and DataOps functions, driving technical excellence, strategic alignment, and innovation across projects, ensuring that best practices and processes are followed, and the quality of data science, data ops and dashboarding solutions are maintained. This role requires deep technical expertise in data science, AI/ML, and data engineering, alongside strong leadership and mentoring capabilities. You will ensure quality delivery, scalable architecture, and ethical use of data while building a high-performing team that supports Reach’s strategic priorities.
Key Focus Areas
1. Strategic & Technical Leadership
- Provide strategic direction and technical leadership across Data Science, AI/ML, and DataOps.
- Define and drive a roadmap aligned with organisational goals, with a focus on digital health, impact measurement, and large-scale behaviour change.
- Guide architectural decisions, assess technical risks, and ensure alignment with broader engineering goals.
- Play an active role in shaping Reach's technology strategy and contribute to organisation-wide KPIs.
2. AI/ML Research & Development
- Drive the design, development, and deployment of high-impact AI/ML models (e.g. LLMs, predictive modelling, NLP, recommendation engines).
- Explore and experiment with emerging technologies, algorithms, and tools to push the boundaries of innovation.
- Champion ethical, responsible AI - ensuring fairness, transparency, and accountability in model design and use.
3. DataOps & Infrastructure
- Oversee the design, development, and automation of scalable and secure data pipelines, workflows, and infrastructure.
- Implement data governance, privacy, and compliance standards across data and model operations.
- Collaborate with SRE, MERL and Engineering teams to ensure seamless integration of data workflows.
4. People Leadership & Team Growth
- Build, manage, and mentor a high-performing team across Data Science, AI/ML, and DataOps disciplines
- Promote a culture of learning, knowledge sharing, and psychological safety.
- Support professional development and growth of individuals and the broader function.
5. Collaboration & Cross-Functional Impact
- Collaborate with cross-functional teams including MERL, SxD, Engineering, and
- Implementation to ensure high-quality delivery and impact.
- Provide data-driven insights and support the development of monitoring tools for real-time evaluation.
- Support on technical input for proposals, concept notes, and partnership development.
6. Operational Excellence & Ways of Working
- Champion process improvements, AI/ML and DataOps practices, and reproducibility standards.
- Promote consistent adoption of best practices across all data and model development workflows.
- Take responsibility for risks, delivery timelines, and the quality of technical outputs.
Responsibilities
- Lead end-to-end development of AI/ML and data solutions, from ideation to deployment and monitoring.
- Design experiments, perform impact analyses, and contribute to data strategy for research and delivery teams.
- Drive continuous evaluation and improvement of models and pipelines.
- Manage team resourcing, performance, recruitment, and onboarding.
- Represent the team internally and externally through conferences, stakeholder engagements, and publications
Qualifications
- Relevant degree in a quantitative or technical field (e.g. Computer Science, Engineering, Statistics, Mathematics, Economics) or equivalent experience.
- Strong mathematical background and a proven track record in data science, data engineering, or AI/ML senior roles.
Skills & Experience Required:
- 7+ years of relevant experience, including 3+ years in a senior or leadership role.
- Strong expertise in data science, data engineering, and AI/ML development.
- Hands-on experience with cloud platforms (AWS, GCP, Azure), DataOps and MLOps tools (CI/CD pipelines, model monitoring), and orchestration tools.
- Strong coding skills (Python, Bash, etc.) and experience with distributed computing.
- Excellent communication and stakeholder engagement skills.
- Demonstrated ability to lead cross-functional, multidisciplinary teams.
Desirable:
- Experience with big data technologies (Spark, Hadoop), containerisation (Docker, Kubernetes), and open-source contributions.
- Knowledge of data privacy regulations and ethical frameworks for AI.