Senior Data Scientist (Machine Learning Engineer) New

At Nielsen, we are passionate about our work to power a better media future for all people by providing powerful insights that drive client decisions and deliver extraordinary results. Our talented, global workforce is dedicated to capturing audience engagement with content - wherever and whenever it’s consumed. Together, we are proudly rooted in our deep legacy as we stand at the forefront of the media revolution. When you join Nielsen, you will join a dynamic team committed to excellence, perseverance, and the ambition to make an impact together. We champion you, because when you succeed, we do too. We enable your best to power our future.
Job Summary
Nielsen is a leading online, radio and television research company specializing in industry leading measurement solutions that provide clients with a comprehensive understanding of the online world. Our Data Science team is experimenting, testing, and driving major insights that impact both a global network of clients and our own Nielsen direction. As a Data Scientist you will be responsible for high-quality design, execution and delivery of global analytics solutions, performance dashboards, and analytical studies. Excited? Come join us!
The Advanced Audience Team focuses on methodologies and pipelines that measure how effectively these campaigns reach targeted audiences, as defined by the client, or via demographics, or even pre-defined audiences from our partners.
The team builds and maintains robust data pipelines that support the advanced analytics needs of Nielsen's audience measurement products that provide value for clients.
The role entails ensuring data quality and accessibility, optimizing data flow and storage, and collaborating with other data scientists to implement scalable solutions for complex data challenges.

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.

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