Senior Data Scientist

Purpose of the role:

The Lead Data Scientist will be responsible for ensuring robust analytical frameworks and methodologies are applied to solving business problems. The ideal candidate will be adept at using data mining and science techniques to analyse large, structured and unstructured data sets to find patterns and insights that will help improve business performance.  The right candidate will have a passion for discovering solutions hidden in large data sets and working with stakeholders to improve business outcomes.

 The lead Data Scientist will also ensure technical requirements and specifications are translated to a non technical audience and will often work along side client services teams to deliver easily accessible client presentations. In addition the lead analyst will mentor and help upskill more junior members of the analytics team.

Key Responsibilities:

• Work closely with client managers and relevant stakeholders to deliver insightful analysis to our clients
• Build, test and deploy machine learning models and frameworks
• Managing the full machine learning lifecycle
• Track and resolve risks, issues, and action items throughout project lifecycle
• Problem-solve with product and client teams, advise on how to leverage data science across the company and for our clients
• Ensuring quality and accuracy of analytics deliverables
• Help guide the development of our internal analytics and data science products.
• Develop technical solutions, frameworks and methodologies to solve key business problems and challenges
• Mentor junior members of the team
• Identify opportunities to improve the development and deployment of machine learning models
• Producing specifications and client facing documentation on technical processes

Key Experience, Skills and Knowledge:

• A degree or equivalent in a numerical subject such as Data Science, Computer Science, Mathematics, Engineering
• Knowledge of commonly used data science and analytics platforms, such as AWS Sagemaker, Data Bricks etc
• Familiar with common data science frameworks such as Spark MLib, TensorFlow, Keras etc
• Experience of using one or more programming languages, R, Python, Scala, Java and SQL.
• Experience using and knowledge of techniques like random forest, gradient boosting, collaborative filtering etc
• Experience managing the end to end analytics and machine learning lifecycle
• Excellent communication skills, both written and verbal. Ability to present complex or highly technical issues in simple and easy-to-understand formats, for technical and nontechnical audiences
• A good understanding of agile product development and software development lifecycle

We are an equal opportunities employer and as such, will make any reasonable adjustments to accommodate the needs of all candidates. If you have any such needs or requirements in the context of your interview, please notify us so that we can make the appropriate arrangements.