Key Responsibilities
1. Business & Strategy Alignment
- Business Translation: Proactively engage with business stakeholders, product managers, and domain experts to deeply understand key organizational challenges and strategic goals.
- Solution Design: Formulate and scope data science initiatives, defining clear objectives, success metrics, and a technical roadmap that directly addresses identified business requirements.
2. End-to-End Model Development & Deployment
- Algorithm Development: Design, prototype, build, and validate machine learning and statistical models from scratch, without reliance on pre-packaged solutions.
- Production Deployment: Implement and manage MLOps pipelines within Azure Machine Learning to ensure reproducible model training, versioning, testing, and continuous deployment into live operational environments.
- Infrastructure: Collaborate with DevOps and engineering teams to ensure algorithms run efficiently and scale reliably within the cloud environment.
3. Data Engineering & Feature Management
- Data Crunching and Manipulation: Execute complex data ingestion, exploration, and feature engineering tasks, applying rigorous statistical methods and domain knowledge to raw and disparate datasets.
- Quality & Integrity: Ensure data integrity throughout the modeling lifecycle, performing extensive Exploratory Data Analysis (EDA) and cleaning to prepare high-quality inputs.
4. Advanced AI & Innovation (Preferred/Plus)
- Generative AI: Explore, experiment with, and deploy large language models (LLMs) and other Gen AI techniques to create new products or optimize existing processes (e.g., semantic search, content generation, synthetic data creation).
- Agentic AI Systems: Investigate and prototype intelligent software agents capable of autonomous decision-making, planning, and tool use, moving beyond simple predictive models.
5. Code Quality & Collaboration
- Write clean, well-documented, and efficient Python code, adhering to software engineering best practices, including unit testing and code reviews.
- Mentor junior team members and contribute to the growth of the team's overall ML/AI knowledge base.
Required Qualifications
- Education: Master's degree or higher in Computer Science, Statistics, Mathematics, or a related quantitative field.
- Programming Mastery: 5+ years of professional experience leveraging Python for data science, including deep expertise in the PyData stack (e.g., Pandas, NumPy, Scikit-learn, TensorFlow/PyTorch).
- Cloud ML Platforms: Proven, hands-on experience building and managing ML models and MLOps workflows specifically using Azure Machine Learning services (e.g., Azure ML Pipelines, Endpoints, Datastores).
- Statistical Rigor: Strong background in statistical modeling, experimental design (A/B testing), and model validation techniques.
- Data Skills: Expert proficiency in SQL and experience working with large-scale, high-velocity data, including ETL/ELT processes and data visualization.
Preferred Qualifications (A Significant Plus)
- Generative AI Experience: Practical experience fine-tuning, RAG-ifying, or deploying modern Large Language Models (LLMs) (e.g., OpenAI, Gemini, Llama).
- Agent Development: Knowledge of agentic frameworks (e.g., LangChain, LlamaIndex) and experience designing multi-step, tool-using autonomous AI workflows.
- Experience with other cloud platforms (AWS Sagemaker, GCP Vertex AI) is beneficial.
- Demonstrated ability to write production-level, highly optimized code for critical systems.
