Note: Client 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.
Job Description
- Join Marketing Services Technology team to develop analytical frameworks and reliable measurement strategies for various products, services, and capabilities.
- Design, execute, and analyse 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 modelling, and predictive analytics, with the ability to operationalise 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 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.