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.

Experience
10-12 yrs