Data Science Senior Manager

Qualifications

Education & Experience

• Advanced degree (Masterʼs or PhD) in Data Science, Computer Science, Statistics, Mathematics, or a related quantitative field.
• 8–12 years of professional experience in data science, analytics, or machine learning, with a demonstrated ability to deliver projects from data ingestion & EDA through to production deployment and monitoring.
• Proven track record of building end-to-end solutions : data acquisition, feature engineering, model development, ML Ops deployment, monitoring, and iterative improvement.
• Hands-on expertise with cloud-based data platforms, particularly the Google Cloud stack (BigQuery for large-scale data warehousing and Vertex AI for ML lifecycle management).
• Experience with alternate ML platforms such as Alteryx, Databricks, H2O.ai, or equivalent tools that support rapid prototyping, automation, and scalable model deployment.
• Strong programming skills in Python, R, and SQL, with experience in productionising models using CI / CD pipelines and containerisation (e.g., Docker, Kubernetes).
• Familiarity with data engineering workflows (ETL / ELT, data pipelines, APIs) to ensure seamless integration between raw data and deployed models.
• Demonstrated ability to support both strategic initiatives and tactical experimentation, including smaller pilots and proofs of concept with ML and AI.
• Industry experience in Media, Telco & Finance preferred but not required.

Job Description

Purpose The Senior Manager, Data Science exists to lead the strategic application of advanced analytics, machine learning, and AI to solve complex business challenges, unlock new opportunities, and drive data-informed decision making across the organization. This role builds and scales a high-performing data science function that delivers measurable impact on revenue growth, operational efficiency, customer experience, and innovation. To succeed, the Senior Manager must cultivate strong relationships with key internal stakeholders including Product, Marketing, Finance, Editorial and Technology, ensuring alignment between data science initiatives and organisational priorities. Externally, the role may engage with technology partners, academic institutions, and industry experts to stay ahead of emerging trends and foster collaboration. This position directly manages a team providing mentorship, guidance, and performance oversight to ensure delivery excellence and career development. Roles that report to this position include :

Data Scientist

Lead the design, development, and deployment of advanced analytics, machine learning, and AI solutions to address strategic business challenges.

Translate complex data into actionable insights that inform executive decision making and drive measurable business outcomes.

Support tactical experimentation by enabling smaller-scale pilots, proofs of concept, and rapid prototyping with ML and AI to test new ideas and validate hypotheses.

Evaluate and adopt emerging technologies, tools, and methodologies to ensure the team remains at the forefront of innovation

Communicate findings and recommendations clearly to both technical and non-technical audiences, including senior leadership. Manage project delivery timelines and resource allocation to ensure successful execution of initiatives. Capabilities & Competencies

Leadership & People Management : Proven ability to inspire, mentor, and grow diverse technical teams.

Strategic Thinking : Strong capability to align data science initiatives with business strategy and long-term goals.

Technical Expertise : Deep knowledge of machine learning, statistical modelling, data engineering, and ML Ops frameworks (CI / CD for ML, monitoring, orchestration tools such as MLflow, Kubeflow, or Vertex AI).

Experimentation Mindset : Ability to design and oversee smaller, tactical experiments and pilots to quickly test and validate new approaches.

Communication : Exceptional ability to distill complex technical concepts into clear, compelling narratives for senior managers and stakeholders.

Collaboration : Skilled at building partnerships across functions and fostering a culture of teamwork and shared success.

Innovation & Curiosity : Demonstrated track record of exploring new technologies and applying creative approaches to problem-solving.

Operational Excellence : Strong project management skills, with a focus on efficiency, scalability, and measurable impact. Key Metrics

Business Impact : Percentage improvement in revenue growth, cost savings, or customer satisfaction attributable to data science initiatives.

Project Delivery : On-time and within-budget completion rate of data science projects.

Model Performance & ML Ops : Accuracy, precision, recall, or other relevant metrics for deployed models; uptime and reliability of ML pipelines; frequency of successful automated retraining cycles.

Stakeholder Engagement : Satisfaction scores from internal stakeholders regarding collaboration and value delivered