Senior Data Scientist

at  Chambers and Partners

SKILLS AND EXPERIENCE

• Delivering significant and valuable advanced analytics projects and/or assets in industry and/or professional services
• Proficiency in programming languages such as Python or R, and extensive experience with ML algorithms and models.
• Experience with cloud services like Azure ML studio, Azure functions, Azure Pipelines, Mlflow, Azure Synapse Analytics, etc., is a plus.
• Experience of working in Azure / Microsoft technical environments is considered a real plus
• Proven understanding of data science methods used for analysing and making sense of Research data outputs and ideally survey data sets
• Fluency in advanced statistics with solid grounding ideally through both education and experience

• Lead and execute complex data science projects using a combination of open-source and cloud tools, driving innovation and delivering actionable insights.
• Develop and deploy advanced machine learning models using cloud-based platforms.
• Collaborate with product managers and designers to ensure the feasibility of product extensions and new products based on existing proprietary, quant and qual datasets
• Work with outputs from Research and historical data to identify consistent and inconsistent product features and document precise requirements for improved consistency
• Collaborate with designers, Tech colleagues and expert users to come up with engaging ways to visualise data and outliers / exceptions for non-technical audiences
• Design and develop novel ways to showcase and highlight key analysis from complex data sets, including joining across datasets which do not perfectly match
• Collaborate with Product, Tech, Research and other Commercial / Internal stakeholders to understand and define a new, marketable product from existing data
• Create and present progress reports and ad-hoc analysis to the key stakeholders and teams
• Constantly think about and explain to stakeholders how analytics “product” could be refined and productionised in future
• Work with Tech colleagues to improve the Data Science workspace, including providing requirements for Data Lake / Data Pipeline / Data Engineering colleagues
• Expanding upon the tools and techniques that have already been developed
• Help us understand our customers (both internal and external) better so we can provide the right solutions to the right people, including proactively suggesting solutions for nebulous problems
• Be responsible for end-to-end Data Science lifecycle : analysis of data, from data cleaning to extracting insights and recommending production approaches
• Responsible for the process of demonstrating value-add to stakeholders
• Coach, guide, and nurture talent within the data science team, fostering growth and skill development.