Sr. Data Scientist-Recommendation Systems

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.

/I & LLM-driven Systers. Apply Generative Al models to enhance personalization and customer facing featores. Design hybrid systems that combine structured Mt models with LLM-generated outputs Evaluate Al-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. Transiate results into actionable insights and model improvements

Data & Workflow Development. Build and curate datasets to support ML and Al applications, Identify opportunities to make data more usable for Al-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 owmership in ambiguous, fast-moving environment

What skills and experience do you need? (Requirements)

Bachelor''s/Master''s degree or equivalent in computer science, data science, statistics, mathematics, analytics, or related discipline.

• 2+ years of proven experience building deep learning models for large scale recommender systems

• Proficiency in ML frameworks such as TensorFlow or Putorch:
  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 Google Cloud Platform
  Experience working with Data Engineering and MOps is desirable.
  High level of independence to develop and own toolkits, pipelines, and dashboards.
  Excellent problem-solving skills and a proactive approach to addressing challenges.
  Strong analytical and critical thinking skills with attention to detail.
  Prior experience in the retail or e-commerce industry is a plus.
  Must be able to learn from others and work collaboratively as part of a highly interdependent