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Head of Data Science...

Experis
London
16 hours ago
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Job Description

Key Responsibilities

  • Shape Data Science Strategy: Define and advise on the data science approach for your product, ensuring a balance of analytical rigor, interpretability, and scalability, while enabling model reuse across multiple client contexts.
  • Client Engagement: Collaborate with sector teams, go-to-market specialists, and solution architects to uncover client challenges, showcase product capabilities, gather feedback, and influence development priorities.
  • Model Deployment: Work closely with engineers to productionize models on cloud platforms (Azure, AWS, or GCP) using MLOps and DevSecOps best practices.
  • Continuous Improvement: Partner with the Product Owner to monitor model performance and user feedback, refining algorithms, enhancing features, and driving better product outcomes over time.
  • Responsible AI: Embed principles of responsible and explainable AI throughout development to ensure outputs are trusted, transparent, and compliant with PwC standards.

    Skills & Experience

  • Applied Analytics Expertise: Hands-on experience (professional or academic) applying analytics to solve real-world business problems.
  • End-to-End Data Science: Practical knowledge across the full lifecycle—from feature engineering and model design to validation, deployment, and monitoring.
  • Technical Proficiency: Fluency in Python, SQL, or similar languages, and experience with deep learning frameworks such as TensorFlow, Keras, PyTorch, or MXNet.
  • Agile & DevSecOps: Familiarity with Agile methodologies and DevSecOps practices, including Git for version control.
  • Cloud Platforms: Exposure to Azure, AWS, or GCP, with a strong interest in building scalable solutions.
  • Communication Skills: Ability to translate complex data concepts for both technical and non-technical audiences, supported by strong data storytelling and visualization capabilities.
  • Analytical Mindset: Intellectual curiosity with a disciplined, hypothesis-driven approach—validating, challenging, and refining outputs for rigor and relevance.
  • Commercial Awareness: A desire to understand how analytics drives business outcomes.
  • Collaborative Approach: Enjoy working in diverse, cross-functional teams with a mix of onshore and offshore resources.

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