Lead Data Scientist
Our client develops industry-leading technology that helps their government and commercial life sciences clients transform data into actionable insights so they can make quicker, more informed decisions.
Reporting to the Head of Technology the Lead Data Scientist will directly manage a team of Data Scientists and will lead the implementation of our client’s Data Science practice. This is a hands-on management position who will have the opportunity to make a material impact and influence on the business, with scope for further growth.
The Lead Data Scientist will be accountable for communicating/evangelizing the Data Science practice (principles, policies, standards, and patterns) to development groups, business groups, and customers and governs adherence to critical guidelines. Additional responsibilities include completing research to understand where new technology/innovation might solve upcoming business problems as well as perform baseline logical reviews on key systems.
● Able to build or test new processes and lead others to do so as well. Expert and independent in relevant Data Science, Data Mining, and/or Statistical frameworks. Able to scope and build best-practices around the usage of Data Science frameworks
● Creates and uses novel data steps to prepare and process data for analysis. Oversees others in the application of data steps.
● Owns best practices in the development and use of core processes. Explores new best practices in non-standard coding languages.
● Develops small and medium complexity project plans. Oversees others in the execution of project tasks.
● Able to apply domain expertise to capture current and likely market trends in discipline, industry and domain. Has a holistic overview of adjacent disciplines, industries and domain.
● Focus on solving challenging problems in natural language processing, machine learning, and information retrieval including topical classification, legal sentiment analysis, user intent detection, open Q&A.
● Research, build, and deploy models based on both shallow and deep machine learning. Train robust NLP-based models.
● Apply machine learning techniques for improving search algorithms.
● Drive best practices for NLP/Machine Learning pipelines.
● Maintain current knowledge base of state-of-the-art ML algorithms (BERT, ELMo, GPT, etc.), API's, and open-source methods and be able to quickly evaluate alternatives.
● Translate complex business requirements into actionable stories with reasonable time estimates.
● Participate in model reviews with key stakeholders, including stakeholders with limited statistical backgrounds from among that set.
● Work with product leaders to apply data science solutions.
● Participate in knowledge sharing sessions (e.g. Guilds and Chapters).