Machine Leaning Engineering (P2)

Company Description

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 Description

About the Role:
As a Machine Learning Engineer at this level, you will be a key contributor to our team, responsible for building and deploying end-to-end machine learning models. You will work with a degree of autonomy on well-defined projects, translating business needs into functional and scalable ML solutions. This role is perfect for hands-on ML Engineers looking to deepen their expertise and take on more complex challenges. 

Responsibilities:
● Independently build, train, and deploy machine learning models for complex projects.
● Design and maintain robust, end-to-end ML pipelines, from data processing to model
serving.
● Contribute to technical design discussions and provide input on system architecture and
best practices.
● Collaborate with business, product, and other engineering teams to understand
requirements and translate them into technical specifications.
● Write high-quality, production-ready code and participate in code reviews to maintain our
standards of excellence.
● Mentor interns or junior engineers, sharing your knowledge and expertise.

Qualifications

Basic Qualifications:
● Bachelor's or Master's degree in Engineering, Mathematics, Statistics, or a related field.
● 3-6 years of professional experience in machine learning engineering.
● Proven experience in building and deploying ML models in a production environment.
● Strong proficiency in Python, SQL, and experience with a distributed computing framework (e.g., Spark).
● Data Analytics Experience: Strong background in data analytics, including statistical analysis, data visualization, and reporting to derive business insights.
● AI Experience: Practical, hands-on experience with modern AI frameworks and techniques, including LLMs or Generative AI applications.
● Familiarity with workflow orchestration tools (e.g., Airflow) and ML platforms (e.g., MLflow) is preferred.
● Knowledge of classification models, regression models, anomaly detection, boosted models, deep learning, and simulation problems, particularly with large datasets.
● Strong problem-solving skills and the ability to work independently on projects.

Additional Information

Preferred Qualifications / Bonus Skills:
Experience with Generative AI models.
Track record of publications in top -tier AI/ML/CV conferences or journals.
Experience working with sports data (broadcast feeds, social media imagery, sponsorship analytics).
Proficiency in cloud computing platforms (AWS, GCP, Azure) and their AI/ML services.
Experience with video processing and analysis techniques.
Familiarity with data pipeline and distributed computing tools (e.g., Apache Spark, Kafka).
Demonstrated ability to lead technical projects and mentor team members.

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