Advanced Modeling & Analysis: Lead the design and implementation of complex ML algorithms (classification, regression, clustering) and Deep Learning models to solve high-impact business problems.
Generative AI & LLM Strategy: Architect and deploy Generative AI solutions, including RAG (Retrieval-Augmented Generation) pipelines. Design advanced prompt engineering strategies and work with LLM APIs (Vertex AI, OpenAI) to build intelligent agents.
Cloud Data Science (GCP): Utilize Vertex AI for the full model lifecycle—from experimentation in notebooks to training, tuning, and deployment. Leverage BigQuery for large-scale data analysis and Cloud Storage for unstructured data management.
Data & Feature Engineering: Collaborate with Data Engineers to build scalable ETL/ELT pipelines. Perform advanced feature engineering and data preparation on massive, complex datasets.
MLOps & Deployment: Oversee the transition of models from research to production. Ensure model stability through monitoring, drift detection, and automated retraining loops.
Cross-Functional Leadership: Act as a technical consultant to Architects and Product Owners. Mentor junior data scientists and engineers, ensuring the team adheres to best practices in experimental design and code quality.
Professional Tenure: 8–10 years of experience as a Data Scientist or ML Engineer, with a proven track record of delivering production-grade AI solutions.
Core Technical Stack: Mastery of Python and its scientific ecosystem (NumPy, Pandas, Scikit-learn, SciPy, Matplotlib).
Advanced SQL & Big Data: Expert-level SQL skills with significant experience querying and manipulating petabyte-scale data in Google BigQuery.
GCP Expertise: Deep hands-on experience with Vertex AI (AutoML, Pipelines, Model Registry, and Training) and Google Cloud Storage.
Generative AI Mastery (Must-Have): * Practical experience building applications with LangChain or LlamaIndex.
Proven expertise in RAG architectures, including vector embeddings and similarity search logic.
Advanced Prompt Engineering skills for optimizing LLM outputs.
Algorithmic Depth: Comprehensive understanding of supervised, unsupervised, and reinforcement learning algorithms, as well as the statistical foundations behind them.
Deep Learning & Frameworks: Strong knowledge of Neural Networks and experience with TensorFlow or PyTorch for specialized deep learning tasks.
Engineering & MLOps: Experience with MLflow or Kubeflow for experiment tracking and model deployment. Familiarity with Docker and Kubernetes for containerized ML workloads.
API & Integration: Basic understanding of RESTful APIs and microservices to ensure models integrate seamlessly into broader software architectures.
Software Best Practices: Proficiency in Git, unit testing for ML code, and reproducible research practices.
Educational Background: Master’s or PhD in Data Science, Statistics, Mathematics, Computer Science, or a related quantitative field.
Soft Skills: Exceptional analytical thinking and the ability to explain complex statistical results to non-technical business stakeholders.
Language Skills: C1
