Lead Data Scientist

3 weeks ago

London, United Kingdom Salt Full time

Lead Data Scientist (Python, ML, NLP, GCP) – Hybrid – London

Day rate: £600 - £900 (inside IR35)

Duration: 3 months

Start: ASAP


My client is looking for a highly skilled and motivated Lead Data Scientist with expertise in Python, Machine Learning (ML), Natural Language Processing (NLP), Deep Learning, Computer Vision, and Google Cloud Platform (GCP) tools. The ideal candidate will have a robust background in building and deploying machine learning models, developing NLP engines, implementing computer vision solutions, and managing MLOps capabilities. This role demands a strong understanding of the digital marketing domain, specifically within Consumer Packaged Goods (CPG), Automotive, Pharmaceutical, and Financial Services sectors. The candidate will be responsible for setting up the guardrails and operating model for AI/ML initiatives, ensuring scalability and reliability across projects.


Key Responsibilities:

  • Domain Expertise & Leadership:
  • Lead the data science team in leveraging AI/ML solutions tailored for digital marketing in CPG, Automotive, Pharma, and Financial Services industries.
  • Establish the AI/ML operating model, including setting up best practices, guidelines, and frameworks for model development and deployment.
  • Define and implement guardrails to ensure compliance, ethical use of AI, and alignment with business objectives.
  • Machine Learning & NLP:
  • Utilize Python and fundamental ML libraries to develop and deploy machine learning models for various applications within the specified domains.
  • Design and implement NLP engines, including data preprocessing, feature engineering, and model building, addressing complex language processing challenges.
  • Develop advanced NLP solutions using deep learning frameworks for Natural Language Understanding (NLU) and Natural Language Generation (NLG).
  • Deep Learning & Computer Vision:
  • Apply deep learning techniques, including the latest advancements in Large Language Models (LLMs), to solve complex problems and drive innovation.
  • Implement Computer Vision techniques and algorithms to extract meaningful insights from visual data, enhancing decision-making processes.
  • MLOps & Deployment:
  • Lead the design and implementation of MLOps pipelines, ensuring efficient model training, deployment, and monitoring across projects.
  • Utilize Docker containerization techniques for seamless deployment and management of machine learning models.
  • Build and maintain CI/CD pipelines to support continuous integration and deployment of data science projects.
  • GCP & Vertex AI:
  • Leverage GCP Data Science tools, with a specific focus on Vertex AI, for building, training, and deploying scalable and reliable models.
  • Ensure that the deployed solutions meet performance, reliability, and security standards.
  • Collaboration & Communication:
  • Work closely with cross-functional teams, including product, engineering, and marketing, to integrate AI/ML solutions into business processes.
  • Communicate complex technical concepts and results to non-technical stakeholders, ensuring clarity and alignment.

Qualifications and Skills:

  • Educational Background:
  • Bachelor’s, Master’s, or Ph.D. in Computer Science, Data Science, Statistics, or a related field.
  • Technical Proficiency:
  • Proficiency in Python programming language with extensive hands-on experience in fundamental ML libraries.
  • Strong understanding and practical experience in building NLP engines, from preprocessing to model deployment.
  • Proven expertise in deep learning frameworks, NLU, NLG, and MLOps capabilities.
  • Demonstrated experience in Docker containerization and building CI/CD pipelines.
  • Solid understanding and practical experience in Computer Vision techniques and applications.
  • GCP & MLOps Expertise:
  • Hands-on experience with GCP Data Science tools, especially Vertex AI, for model building, training, and deployment.
  • In-depth knowledge of MLOps principles, including lifecycle management and automation.

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