What You’ll Do
As a Data Scientist – Recommendation Systems, you will own and drive high-impact ML systems powering personalization across Glance products.
- Design, build, and deploy large-scale recommendation and personalization models using diverse, high-volume data sources.
- Lead rapid experimentation—from hypothesis formulation to offline evaluation and online A/B testing—balancing speed, rigor, 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 monetization.
- 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.
What We’re Looking For
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 → prototype → production → business impact.
Experience & Skills
- 10+ years of industry experience in ML/Data Science building large-scale recommendation or personalization 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.