Lead AI / ML Data Scientist

Job Description

At Nielsen, we are seeking a Data Scientist to join our team. Are you passionate about pushing the boundaries with the latest advancements in AI / ML? Does the prospect of applying cutting-edge AI research to develop industry-defining software solutions for audience measurement excite you? In this role, you will be at the forefront of our mission, leveraging sophisticated machine learning and AI to deliver a comprehensive understanding of audience behavior. You will architect and implement AI / ML systems that unlock novel insights from complex audience data.

Core Responsibilities :

Project Leadership : Take ownership of specific data science projects, guiding junior team members through the entire project lifecycle, from problem definition and data exploration to model deployment and evaluation.

Solution Design & Development : Design and implement complex analytical solutions, including advanced machine learning models, statistical frameworks, and algorithmic approaches to address specific business problems in media and audience measurement.

Mentorship & Knowledge Transfer : Actively mentor Data Scientists, providing technical guidance, code reviews, and best practices. Promote a culture of continuous learning and knowledge sharing within the team.

Research & Innovation : Stay abreast of the latest advancements in data science and apply relevant techniques to improve existing models and develop innovative solutions. Conduct exploratory data analysis to uncover new insights.

Collaboration : Work closely with Data Engineers to ensure data availability and quality, and with the Data Visualization Specialist to effectively communicate findings.

Qualifications

Audience Segmentation & Profiling : Expertise in developing robust audience segments based on demographics, behaviors, interests, and consumption patterns across various media platforms.

Content Optimization & Personalization : Strong background in analyzing content performance, identifying engagement drivers, and developing recommendation engines or personalization algorithms.

Ad Effectiveness & Measurement : Proven ability to design and execute studies measuring the impact of advertising campaigns, including attribution modeling, lift analysis, and ROI calculation.

Predictive Analytics & Forecasting : Experience in building predictive models for audience behavior, future trends, content virality, or advertising spend.

Causal Inference : Deep understanding of experimental design (A / B testing, multivariate testing) and quasi-experimental methods to establish causality between interventions and outcomes.

Advanced Programming & Modeling : Highly proficient in Python and / or R, with extensive experience in machine learning libraries (e.g., scikit-learn, TensorFlow, PyTorch), statistical modeling packages, and big data framework

Additional Information

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