Senior Manager - Applied Scientist

Responsibilities:

• Design, build, and deploy large-scale recommendation and personalisation models using diverse, high-volume data sources.
• Lead rapid experimentation from hypothesis formulation to offline evaluation and online A/B testing, balancing speed, rigour, and business impact.
• Develop and productionize end-to-end ML pipelines, including data preparation, feature engineering, model training, evaluation, and monitoring.
• Partner closely with Product, Engineering, Design, UX, and Business teams to translate product goals into scalable ML solutions.
• Monitor model health and performance, applying statistical techniques to ensure robustness and long-term effectiveness.
• Explore and prototype new ML techniques to improve relevance, engagement, and monetisation.
• Act as a technical interface for stakeholders, clearly articulating trade-offs, results, and next steps.
• Contribute to Glance's thought leadership through blogs, case studies, and industry conference talks.

The core requirements for the job include the following:

Core Expectations:

• Deep expertise in Machine Learning, Data Science, and Recommendation Systems at scale.
• Strong applied understanding of experimentation, metrics, and causal reasoning in real-world systems.
• Ability to take models from idea to prototype, production, and business impact.

Experience and Skills:

• 10+ years of industry experience in ML/Data Science building large-scale recommendation or personalisation systems.
• Hands-on experience applying techniques from ML, Deep Learning, NLP, Reinforcement Learning, Time Series, and Statistics.
• Strong programming skills in Python with production-quality code practices.
• Experience with big data ecosystems, especially Apache Spark.
• Familiarity with cloud platforms such as AWS, GCP (Vertex AI), or Azure.
• Experience operating in identity-constrained / privacy-aware environments (e. g., iOS/Android, identity-less systems) is a plus.
• Excellent communication skills; able to explain complex technical ideas clearly to non-technical stakeholders.
• High curiosity, strong problem-solving ability, and a bias toward learning and experimentation.

Qualifications:

• Bachelor's or Master's degree in a quantitative field such as Computer Science, Electrical Engineering, Statistics, Mathematics, Operations Research, Economics, Analytics, or Data Science.
• PhD is a plus, but not mandatory.
• We value diverse academic backgrounds; great data scientists come from many disciplines.