Job Description

About the Role

We are hiring a Data Scientist who is passionate about solving complex business problems using data, machine learning, and AI. The ideal candidate has a strong foundation in Python, hands-on experience with ML frameworks, and exposure to Microsoft Azure cloud services. You will work on developing scalable ML models, deploying AI solutions, and deriving actionable insights from large datasets.

Key Responsibilities

• Design, build, and evaluate machine learning models for classification, regression, forecasting, and NLP use cases.

• Develop and maintain data pipelines using Python and Azure Data Factory / Azure Databricks for ETL and feature engineering.

• Deploy ML models on Azure Machine Learning (Azure ML) using endpoints, pipelines, and MLflow tracking.

• Collaborate with data engineers to ensure data quality, availability, and governance across Azure Data Lake and Azure Synapse Analytics.

• Apply AI/GenAI capabilities (Azure OpenAI, Cognitive Services) to build intelligent applications and automation workflows.

• Monitor model performance in production, identify drift, and implement retraining strategies.

• Translate business requirements into data science problem statements and communicate findings to stakeholders.

• Participate in code reviews, documentation, and adherence to ML Ops best practices.

Required Skills & Qualifications

• Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or related field.

• 4+ years of professional or project-based experience in data science or machine learning.

• Strong proficiency in Python (pandas, NumPy, scikit-learn, XGBoost, LightGBM).

• Hands-on experience with Microsoft Azure services: Azure ML, Azure Databricks, Azure Data Factory, Azure Blob Storage, or Azure Synapse.

• Understanding of supervised and unsupervised learning algorithms, model evaluation, and hyperparameter tuning.

• Experience with deep learning frameworks: TensorFlow or PyTorch (at least one required).

• Solid SQL skills for querying relational databases and analytical processing.

• Familiarity with MLOps practices: experiment tracking (MLflow), model versioning, and CI/CD for ML.

• Experience with data visualization tools (Power BI, Matplotlib, Seaborn, or Plotly).

Good to Have

• Microsoft Azure certifications: AZ-900, AI-900, DP-100 (Azure Data Scientist Associate) preferred.

• Experience with NLP libraries (Hugging Face, spaCy, NLTK) and LLM integrations (Azure OpenAI, LangChain).

• Knowledge of containerization and deployment: Docker, Kubernetes, or Azure Container Instances.

• Familiarity with big data tools: Apache Spark (PySpark) via Azure Databricks.

• Exposure to Generative AI, RAG (Retrieval-Augmented Generation), or Prompt Engineering.

• Version control using Git and experience with Agile/Scrum development methodology.

Technical Stack

Languages

Python, SQL

ML/AI Frameworks

scikit-learn, XGBoost, TensorFlow, PyTorch, Hugging Face

Cloud Platform

Microsoft Azure (Azure ML, Databricks, Data Factory, Synapse, OpenAI)

MLOps Tools

MLflow, Azure DevOps, GitHub Actions

Data & BI Tools

Power BI, Pandas, PySpark, Jupyter

Storage & DB

Azure Blob Storage, Azure Data Lake, SQL Server, Cosmos DB

What We Offer

• Competitive salary and performance-based incentives.

• Azure certification sponsorship and continuous learning budget.

• Mentorship from senior data scientists and ML architects.

• Exposure to cutting-edge AI/ML projects across domains.

• Flexible hybrid working model and collaborative culture.