Machine Learning Engineer

Responsibilities

• Design, develop, optimize, and maintain robust data architecture, methodologies, and pipelines, aligning with ETL principles and Nielsen's business objectives.
• Refactor research software prototypes to production grade standards, improving performance and reliability.
• Tackle complex data challenges to deliver actionable insights, aiding in the achievement of organizational goals and meaningful client impact.
• Develop data science methodologies and solutions that solve key client problems, and that are reproducible and scalable. 
• Ensure the integration of disparate data sources into a cohesive and efficient data ecosystem, facilitating seamless data accessibility and interoperability for various analytical platforms and stakeholders throughout Nielsen.
• Offer mentorship, advice, and coaching to other professionals in data and analytics on best practices and standards.
• Lead in the evaluation and deployment of cutting-edge methodologies and processes in data science, boosting the team's overall efficiency and effectiveness.
• Collaborate with business analysts and solutions architects to devise technical architectures for key enterprise projects and initiatives that improve client experience.
• Engage in continuous learning in areas like engineering, machine learning and data science.

Qualifications

• Master's degree in Data Science, Computer Science, Statistics or Mathematics, Operation Research, or other hard sciences) with outstanding analytical expertise and strong technical leadership skills.
• Excellent software engineering fundamentals and debugging skills.
• Significant experience with collaborative code development, including version control, unit/integration testing, code review and sharing.
• Significant experience working in a cloud-based environment, ideally AWS.
• Strong machine learning fundamentals.
• Proficient in Python.
• Have a thirst for new challenges. The candidate should be comfortable diving into the unknown and making sense of what's noise and what isn't.
• Have an eye for design at multiple levels: Infrastructure architecture (cloud infrastructure), ETL data pipelines, and analytic pipelines.
• Have Experience building analytic methodologies: either from scratch, using existing software libraries and APls, and/or combinations of the two.
• Feel comfortable and excited to bring innovative ideas and challenge existing methods and processes. This requires the ability to pick apart the implementation details from the overall design.
• Help incubate a forward-thinking culture in a growing data-science organization. This includes prioritizing staying up-to-date with new software, methodologies, and industry research and facilitating the cross­ pollination of ideas across individuals and teams.
• Be self-motivated with a keen problem-solving aptitude and a continuous learning mindset.
• Demonstrates a strong work ethic, capable of working abstractly.
• Strong communication (verbal and written) and presentation skills in English.