Lead Data Scientist

Job Summary

As a Lead Data Scientist , you will take ownership of the technical vision and execution for the next-generation marketing platform. Your role will involve leading the integration of advanced models, providing mentorship, and driving the implementation of state-of-the-art technologies in a collaborative environment aimed at scaling products for a vast user base.

Responsibilities

• Unified AI Strategy & Technical Leadership:

  • Design and own the end-to-end technical vision for the marketing platform, integrating recommender systems and large language models (LLMs).
  • Lead research and implementation of innovative models that merge predictive signals with generative capabilities.
  • Champion best practices across modeling stacks including classical machine learning fundamentals and MLOps.
  • Act as a technical mentor for data scientists in areas such as feature engineering and LLM fine-tuning.

• Recommender System Innovation & Optimization:

  • Build and deploy large-scale recommender systems utilizing techniques like collaborative filtering and matrix factorization.
  • Address challenges such as the cold-start problem and implement A/B testing for evaluation of recommendation systems.
  • Apply classical machine learning techniques to predict user behavior for enhancing recommendation engines.

• Generative AI & LLM Integration:

  • Develop LLM-powered features to enhance platform offerings, including campaign optimizers and natural language interfaces.
  • Fine-tune pre-trained LLMs on proprietary data to enhance relevance and factual accuracy.
  • Design Retrieval-Augmented Generation (RAG) pipelines for improved product catalog reasoning.

• Cross-Functional Influence & Execution:

  • Collaborate with product, engineering, and design teams to translate business goals into technical roadmaps.
  • Effectively communicate complex technical concepts to a diverse audience, including junior engineers and executives.
  • Lead projects from ideation to production, ensuring scalability and maintainability of models.

Qualifications

• Educational Requirements:

  • Bachelor’s/Master’s degree or PhD in Computer Science, Statistics, Mathematics, or a related quantitative field.

• Experience:

  • 7+ years of hands-on experience in building and deploying machine learning models in a business context.
  • 2+ years of experience in developing solutions using Large Language Models.

• Technical Skills:

  • Expert-level proficiency in Python and data science libraries including Pandas, NumPy, and Scikit-learn.
  • Advanced proficiency in SQL for handling large datasets.
  • Proven experience in leading complex data science projects with significant business impact.

Preferred Skills

• Experience with cloud-based ML platforms like AWS SageMaker or MLflow.
• Hands-on experience with vector databases.
• Familiarity with frameworks such as LangChain for LLM applications.
• Understanding of LLM operational issues including cost management and responsible AI principles.

Experience

• A minimum of 7 years of relevant experience in data science and machine learning within a professional environment.

Environment

The typical work setting involves collaboration within a dynamic and innovative team dedicated to scaling technology solutions for millions of users.

Salary

Estimated salary range is not specified.

Benefits

• Benefits specifics are not provided but can include standard offerings such as health insurance, paid leave, and professional development opportunities.