Principal Data Scientist - Search

We are seeking a talented Principal Data Scientist to lead the development of Product Search and Recommendation systems. The ideal candidate will have a strong background in natural language processing (NLP), machine learning, deep learning, and semantic understanding, and will be passionate about transforming the way users discover relevant content. As a Principal Data Scientist, you will be responsible for driving the vision and strategy for Ecommerce search and personalized recommendation systems, leveraging state-of-the-art techniques such as transformer models, embeddings, and knowledge graphs. You will collaborate closely with product teams, engineers, and business stakeholders to enhance user experience and business outcomes.

Roles & Responsibilities

Leadership & Strategy

Lead the development of cutting-edge Search and recommendation algorithms that improve relevance, personalization, and user engagement.

Define and execute the strategy for semantic understanding and recommendations, aligning with overall business goals.

Work cross-functionally with product managers, engineers, and data scientists to drive the roadmap for search and recommendation improvements.

Search & NLP Expertise

Lead the design and implementation of Search/Recommendation models that go beyond traditional keyword matching to understand user intent and context.

Apply advanced NLP techniques such as transformers (BERT, GPT, T5), word embeddings, and contextualized word representations to enhance search relevance.

Use techniques like sentence embeddings, document embeddings, and similarity measures to build scalable search systems that understand semantic meaning.

Model Development & Experimentation

Conduct research and experiments to design, develop, and validate new Search models and Recommendation techniques.

Continuously test, measure, and optimize models through A/B testing, real-time metrics, and user feedback loops.

Develop approaches to handle large-scale data, ensuring the models can be deployed efficiently in production environments.

Collaboration & Mentorship

Lead and mentor a team of data scientists, guiding them in the development of semantic models and complex recommendation algorithms.

Foster a culture of knowledge sharing and collaboration across teams to ensure that the best practices are followe'd in model development and deployment.

Collaborate with engineers & product managers to ensure the effective integration of models into scalable production systems.

Thought Leadership

Stay up to date with the latest research in Search, NLP, and recommendation systems.

Evangelize the use of cutting-edge techniques within the company to drive innovation in search and recommendations.

Years of Experience

8 years of experience executing and deploying data science, machine learning, deep learning, and generative AI solutions, preferably in a large-scale enterprise setting (fewer years may be accepted with a masters or doctorate degree)

8 years of programming experience (fewer years may be accepted with a masters or doctorate degree)

5 years of SQL experience and knowledge of various statistical modeling or machine learning techniques

Bachelors degree in mathematics, statistics, physics, economics, engineering, computer science, data or information science, or related quantitative analytic field (or equivalent work experience in lieu of degree)

Candidates with Doctorate or masters degree are preferred

Education Qualification & Certifications

bachelors degree (Required): Mathematics, Statistics, Physics, Economics, Engineering, Computer Science, Data or Information Science, or related quantitative analytic field (or equivalent work experience in a related field)

Doctorate Degree (Preferred): Mathematics, Statistics, Physics, Economics, Engineering, Computer Science, Data or Information Science, or related quantitative analytic field

Skill Set Required

Machine Learning & AI

Supervised/unsupervised learning (eg, regression, clustering)

Deep learning (CNNs, RNNs, Transformers)

Natural Language Processing (NLP) for search relevance

Experience with generating vector embeddings

Statistical & Mathematical Expertise

Probability, statistics, and A/B testing

Leadership & Collaboration

Cross-functional team collaboration (engineering, product, design)

Mentoring junior data scientists

Communicating technical concepts to non-technical stakeholders

Tools & Frameworks

Programming (Python, R, Scala)

ML frameworks (TensorFlow, PyTorch, scikit-learn)

Version control (Git)

Performance Evaluation

Model evaluation using metrics (precision, recall, NDCG)

Online learning and incremental model updates

Understanding of Data Engineering & Infrastructure

Big data technologies (Apache Spark, Hadoop)

Data pipeline management (ETL processes)

Database management (SQL, NoSQL, Elasticsearch)

• Cloud platforms (AWS/GCP/Azure)