Senior Data Scientist II

Nielsen is actively seeking passionate Data Scientists and data science managers at all levels to join our team. In this role, you will be at the forefront of applying cutting-edge AI/ML research to develop industry-defining software solutions for audience measurement. You will leverage sophisticated AI/ML to deliver a comprehensive understanding of audience behavior, architecting and implementing AI/ML systems that unlock novel insights from complex audience data. This position involves collaborating with a diverse data science and engineering team, contributing to the Nielsen ML community through scientific papers, patents, mentoring other ML-interested professionals, and providing ML consultation.

Responsibilities:

• Project Leadership: Lead data science projects, guiding junior team members from problem definition to model deployment.
• Core DS Skills: Have a strong understanding of core data science algorithms and their applications. DL and generative AI work is desirable.
• Solution Design and Development: Design and implement advanced analytical solutions, including ML models and statistical frameworks, for media and audience measurement.
• Mentorship and Knowledge Transfer: Mentor data scientists, provide technical guidance, perform code reviews, and foster continuous learning.
• Research and Innovation: Stay current with data science advancements, applying new techniques to improve models, develop solutions, and uncover insights.
• Collaboration: Work closely with data engineers to ensure data availability and quality and with the data visualization specialist to effectively communicate findings.

Core Expertise:

• Audience Segmentation and Profiling: Develop sophisticated audience segments by analyzing demographics, behaviors, interests, and consumption patterns across diverse media platforms.
• Content Optimization and Personalization: Analyze content performance to identify engagement drivers and build recommendation engines or personalization algorithms.
• Ad Effectiveness and Measurement: Design and execute studies to measure the impact of advertising campaigns, including attribution modeling, lift analysis, and ROI calculation.
• Predictive Analytics and Forecasting: Create predictive models for audience behavior, future trends, content virality, or advertising spend.
• Causal Inference: Apply experimental design (A/B testing, multivariate testing) and quasi-experimental methods to establish causality between interventions and outcomes.

Requirements:

• Experience Level: For individual contributors, 3-10+ years of experience building machine learning models for business applications (hiring at all levels). For managers, 10+ years of experience.
• Proficiency in: Programming languages (Java, C++, and Python) and modeling tools (R, scikit-learn, Spark MLLib, MXNet, TensorFlow, NumPy, and SciPy).
• Familiarity with large-scale distributed systems (Hadoop, Spark).
• Education: Master's degree in mathematics, graph theory, operations research, statistics, engineering, or a related quantitative discipline, or a PhD.
• 7-12 years of experience building machine learning models for business applications.
• The ideal candidate is proficient in programming languages (Java, C++, and Python); experienced with modeling tools (R, scikit-learn, Spark MLLib, MxNet, TensorFlow, NumPy, and SciPy); and familiar with large-scale distributed systems (Hadoop and Spark).
• Master's degree in mathematics, statistics, engineering, or a related quantitative discipline, or a PhD