Chief Data Analytics Leader
3 weeks ago
We are seeking a highly skilled Senior AI Research Scientist to join our team at Wood Mackenzie. This is an exceptional opportunity for a talented individual to lead advanced analytics projects, develop predictive models, and generate actionable insights to optimize our operations and drive business growth.
This role will be working within our AI group and focus on the research, development, and delivery of generative AI solutions within our products. You will provide strategic leadership and mentorship within a cross-functional team of data scientists, software engineers, data engineers, and product managers that deliver our product roadmap.
The successful candidate for this role must have a strong Data Science background and understand the challenges of delivering data products with a commitment to incremental delivery. Proven experience in leading data science projects and the ability to articulate AI/ML/DS concepts effectively across multiple business areas is essential.
Responsibilities- Lead usage and implementation of AI solutions, including generative AI, to drive innovation and solve complex business problems
- Work with our product team to understand the problems our customers face, design solutions to solve them, and ensure alignment with our strategic objectives
- Use ML/AI and modelling techniques and apply appropriate data engineering, data mining, and statistical concepts
- Write maintainable, testable, and optimized code - and help junior team members do the same
- Collaborate with teams deploying data science solutions within their product area and identify feedback that will improve the platform
We use a wide variety of tools and technologies across our products, for this role we're looking for: Strong skills in Python and AI/ML frameworks like TensorFlow, PyTorch, and Langchain. Deep understanding of big data technologies (e.g. Spark, Hadoop). Familiarity with ML-adjacent technologies like AWS Sagemaker and Apache Airflow. Experience with data preprocessing, data wrangling, and data augmentation techniques. Proficiency in using cloud platforms like AWS, Google Cloud, or Azure for deploying scalable AI solutions. Understanding of MLOps practices, model deployment, and monitoring in production environments. Experience with Docker, Kubernetes, and CI/CD pipelines.
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