AI Data Scientist - Applied AI

Overview

Senior IT Data Scientist responsible for leading, developing and implementing data-driven solutions to complex business problems, owning projects from inception to deployment.

What you'll do

• Independently identify analytical opportunities and conduct in-depth data exploration and analysis.
• Build, test and deploy predictive models using various machine learning techniques.
• Lead the development and deployment of models, working independently on data science projects from start to finish.
• Identify and solve intermediate to complex data problems, validating models and ensuring their accuracy with minimal supervision.
• Engage with broader business units to clearly understand and meet their data needs.
• Present analytical findings and insights to various stakeholders, including non-technical audiences.
• Drive medium-sized projects that directly influence business decisions and ensure alignment with broader business objectives and strategies.
• Handle larger, more complex datasets, develop new predictive models from scratch and optimize existing ones to improve performance.
• Apply proficiency in multiple programming languages and advanced data manipulation techniques.
• Leverage knowledge of advanced statistical methods and machine learning algorithms to address business questions.

What you'll need

• Demonstrated experience in independently identifying analytical opportunities, conducting in-depth data exploration, and owning projects from inception to deployment.
• Proven track record of driving medium-sized projects that influence business decisions and align with strategic objectives.
• Proficient in multiple programming languages and advanced data manipulation.
• Strong, in-depth knowledge of advanced statistical methods and machine learning algorithms.
• Strong data visualization and statistical modeling skills to support comprehensive data analysis.
• Exceptional communication skills, with the ability to present complex findings clearly to various stakeholders, including non-technical audiences.
• Strong collaborative capabilities to effectively engage with broader business units to understand and meet data needs.