Data Scientist

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.