Data Scientist

Responsibilities:

• Project Leadership: Lead data science projects, guiding junior team members from problem definition to model deployment.
• 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, conduct 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 Visualisation Specialist to effectively communicate findings. Core Expertise:
• Audience Segmentation and Profiling: Develop sophisticated audience segments by analysing demographics, behaviours, interests, and consumption patterns across diverse media platforms.
• Content Optimisation and Personalisation: Analyse content performance to identify engagement drivers and build recommendation engines or personalisation algorithms.
• Ad Effectiveness and Measurement: Design and execute studies to measure the impact of advertising campaigns, including attribution modelling, lift analysis, and ROI calculation.
• Predictive Analytics and Forecasting: Create predictive models for audience behaviour, 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++, Python) and modelling tools (R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, 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.
• 4-10+ years of experience building machine learning models for business applications.
• The ideal candidate is proficient in programming languages (Java, C++, Python), experienced with modelling tools (R, scikit-learn, Spark MLLib, MxNet, Tensorflow, numpy, scipy), and familiar with large-scale distributed systems (Hadoop, Spark).
• Master's degree in mathematics, statistics, engineering, or a related quantitative discipline, or a PhD.
• Core DS Skills: Have a strong understanding of core data science algorithms and their applications. Experience with Deep Learning (DL) and Generative AI is desirable.