Associate Software Engineer - Data Scientist
Full-Time · 4-5 Years Experience
Department
Data Science & AI
Location
Hybrid / On-site
Experience
4-5 Years
Employment Type
Full-Time
Notice Period
Immediate Joiners Preferred
About the Role
We are hiring a Junior 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.
- 0–2 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.
Skills Required
- Bachelor's or Master's degree in Computer Science, Data Science, Engineering, Mathematics, or related field
- 0-2 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, Azure Synapse
- Understanding of supervised and unsupervised learning algorithms, model evaluation, and hyperparameter tuning
- Experience with deep learning frameworks: TensorFlow or PyTorch
- 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)
- Translate business requirements into data science problem statements and communicate findings to stakeholders
- Participate in code reviews, documentation, and adhere to MLOps best practices
- Microsoft Azure certifications: AZ-900, AI-900, DP-100
- 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
Johnson Controls Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Johnson Controls and has not been reviewed or approved by Johnson Controls.
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Retirement Support — Retirement support is positioned as a meaningful part of the package through employer 401(k) matching, repeatedly framed as a strong pillar of the overall rewards mix. The matching contribution is described with specific match levels in multiple places, reinforcing perceived value for long-term saving.
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Leave & Time Off Breadth — Time off is presented as comparatively robust, with multiple paid holiday categories, vacation time, and sick time described as generous or “amazing” in places. Paid time off breadth appears to be a consistent contributor to total rewards attractiveness beyond base pay.
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Flexible Benefits — Benefits are described as broad and customizable, spanning standard medical/dental/vision plus optional add-ons like pet insurance, identity protection, and legal support. Tuition reimbursement is repeatedly highlighted as a high-value option supporting professional development.
Johnson Controls Insights
What We Do
At Johnson Controls, we transform the environments where people live, work, learn and play. From optimizing building performance to improving safety and enhancing comfort, we drive the outcomes that matter most. Dedicated to protecting the environment, we deliver our promise in industries such as healthcare, education, data centers and manufacturing. With a global team of 100,000 experts in more than 150 countries and over 130 years of innovation, we are the power behind our customers’ mission. Our leading portfolio of building technology and solutions includes some of the most trusted names in the industry, such as Tyco®, York®, Metasys®, Ruskin®, Titus®, Frick®, Penn®, Sabroe®, Simplex®, Ansul® and Grinnell®.
