Must Have Technical/Functional Skills
2+ years of proven experience building deep learning models for large-scale recommender systems.
Proficiency in ML frameworks such as TensorFlow or PyTorch.
Proficiency in SQL, Python and Spark for data analysis and manipulation. Experience working with Databricks is a plus.
Proficiency with statistics, design of experiments, exploratory data analysis, and insights generation.
Experience working with LLMs or Generative systems in applied settings.
Experience working with cloud platforms like Azure or GCP.
Experience working with Data Engineering and MLOps is desirable.
High level of independence to develop and own toolkits, pipelines, and dashboards.

Roles & Responsibilities
Recommender Systems Development. Design and improve recommender systems for customer experiences. Develop models that combine user behavior, product data, and contextual signals. Partner with engineering to deploy and iterate on models in production.
AI & LLM-driven Systems. Apply Generative AI models to enhance personalization and customer facing features. Design hybrid systems that combine structured ML models with LLM-generated outputs. Evaluate AI-driven outputs using business and user-centric metrics.
Experimentation & Measurement. Design and run A/B tests and segmentation-based experiments. Develop measurement frameworks for personalization. Translate results into actionable insights and model improvements.
Data & Workflow Development. Build and curate datasets to support ML and AI applications. Identify opportunities to make data more usable for AI-driven systems. Partner with engineering teams to develop scalable pipelines.
Cross-functional Collaboration. Work closely with product owners, engineers, and data scientists to define and deliver solutions. Communicate technical findings clearly to both technical and non-technical audiences. Operate with high ownership in ambiguous, fast-moving environment.

Senior Data scientist

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