Data Scientist

We are looking for candidates with strong Python-based data science and machine learning experience, combined with hands-on exposure to modern AI/LLM frameworks and agentic AI development on Cloudera / Databricks.

In this role, you will:

• Join Technology team to develop analytical frameworks and reliable measurement strategies for various products, services, and capabilities.
• Design, execute, and analyze complex business and user experiments
• Partner with Product partners and other Data Engineers to set the vision and develop experimentation specifically focused on Profiling Engine, Advanced Segmentation Engine and Advanced Targeting.
• Communicate key insights from analyses, experiments, and data products to stakeholders.

• Core Data Science & Analytics Demonstrate strong expertise in data exploration, feature engineering, statistical modeling, and predictive analytics, with the ability to operationalize models in production environments.
• Have deep proficiency in Python (preferred) and/or R, with experience using modern data science libraries such as NumPy, Pandas, Scikit-learn, and PyTorch or TensorFlow.
• Be highly proficient in SQL and experienced in working with large-scale data warehouses and data pipelines.

Machine Learning & AI Engineering:

• Possess strong experience developing, evaluating, and deploying machine learning and deep learning models across the model lifecycle.
• Experience building and deploying models using modern ML and MLOps practices, including experiment tracking, model versioning, CI/CD for ML, and monitoring.
• Familiarity with cloud-based ML platforms (AWS preferred) and distributed data processing frameworks (e.g., Spark).

Generative AI & Agentic Systems:

• Hands-on experience with Large Language Models (LLMs) and Generative AI frameworks, including prompt engineering, retrieval-augmented generation (RAG), and model orchestration.
• Experience building AI agents or agentic workflows capable of reasoning, tool use, multi-step task execution, and autonomous decision-making.
• Familiarity with LLM application frameworks (e.g., LangChain, LlamaIndex, or similar orchestration frameworks).
• Experience with vector databases, embeddings, and semantic search for building knowledge-driven AI systems.

Agentic Coding & AI-Assisted Development:

• Strong understanding of AI-assisted software development workflows, including agent-based coding, code generation, automated debugging, and evaluation loops.
• Experience integrating LLMs with APIs, internal tools, and data systems to build production-grade AI copilots or autonomous workflows.

Business Impact & Communication:

• Ability to translate complex technical concepts into clear business insights, communicating effectively with both technical and non-technical stakeholders.
• Strong analytical thinking with the ability to work with ambiguous or incomplete data, develop creative analytical approaches, and connect results to business outcomes.

Domain & Collaboration:

• Experience applying data science in digital marketing, customer analytics, or growth analytics is highly desirable but not mandatory.
• Comfortable collaborating with engineering, product, and business teams to build scalable data products and AI-driven solutions.