Data Scientist II at Johnson Controls

Pune, maharashtra, India -

Full Time

Start Date

Immediate

Expiry Date

18 Oct, 26

Salary

0.0

Posted On

20 Jul, 26

Experience

2 year(s) or above

Remote Job

Yes

Telecommute

Yes

Sponsor Visa

Skills

Python, Machine Learning, Microsoft Azure, SQL, TensorFlow, PyTorch, MLOps, NLP, Azure Databricks, Azure Machine Learning, Data Pipelines, Power BI, Scikit-learn, XGBoost, Deep Learning, Generative AI

Industry

Industrial Machinery Manufacturing

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.

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Responsibilities

Design and deploy scalable machine learning models and AI solutions using the Microsoft Azure ecosystem. Develop data pipelines and collaborate with engineers to derive actionable business insights from large datasets.