Honeywell hiring for Data Scientist jobs

Honeywell hiring for Data Scientist jobs

Company: Honeywell

Qualifications: Bachelor’s/Advanced degree

Experience Needed: Freshers

Location: Bangalore 

✅ Basic Qualifications

• Experience: 0+ years → Open for freshers or early-career professionals

• Languages: Proficiency in Python or R

• Skills: Strong analytical and problem-solving capabilities

💡 This is ideal for recent graduates with good hands-on academic or internship projects.

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🎓 Preferred Qualifications

• Degree: Bachelor’s or Master’s in:
• Computer Science
• Statistics
• Mathematics
• Or similar technical fields

• Preferred Experience:
• Prior data science projects (college, internships, freelancing, Kaggle, etc.)
• Collaborating with cross-functional teams (like product, engineering, or domain experts)
• Ability to think strategically and align your insights with business goals

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🔍 Key Responsibilities (Implied from Qualifications)

• Apply ML/statistical models to real-world problems
• Interpret data to drive actionable insights
• Communicate results with business and technical teams
• Potentially work across cross-functional teams (e.g., data engineers, product managers)

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🧠 Interview Questions – Tips & Expected Answers

Here's how to prep effectively for the listed questions:

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1. Supervised vs. Unsupervised Learning

• Supervised: Uses labeled data (e.g., classification, regression)
• Unsupervised: No labels, finds patterns (e.g., clustering, dimensionality reduction)

📌 Example:

“I used supervised learning (Random Forest) for churn prediction and unsupervised learning (K-Means) to segment customers.”

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2. Python/R for Data Analysis

“In Python, I used pandas, numpy for data cleaning and transformation, and scikit-learn for model building. I also used matplotlib and seaborn for visualizations.”

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3. End-to-End Project Handling

• Data collection → Cleaning → EDA → Model training → Evaluation → Deployment

“In my final year project, I predicted housing prices. I used a linear regression model with scikit-learn, cross-validated results, and deployed it using Flask.”

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4. Handling Imbalanced Data

• Techniques: SMOTE, Random Oversampling/Undersampling, Class Weights

“I used SMOTE to balance a fraud detection dataset, which significantly improved recall without sacrificing precision.”

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5. Analytical Thinking Example

“In a college project, we had inconsistent customer feedback data. I built a text preprocessing pipeline that filtered out noise and improved sentiment classification accuracy by 12%.”

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6. Choosing the Right Algorithm

• Depends on data size, feature type, accuracy vs. speed, interpretability

“For interpretability, I prefer logistic regression. For performance, Random Forest or XGBoost. I test multiple models and use cross-validation to decide.”

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7. Cross-Functional Collaboration

“During my internship, I worked with marketing and product teams to define KPIs for customer engagement and built a dashboard for weekly reporting.”

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8. Model Evaluation Steps

• Metrics: Accuracy, Precision, Recall, F1-score, ROC-AUC
• Tools: Confusion matrix, Cross-validation, Learning curves
• “I always check for overfitting using cross-validation and monitor precision-recall trade-off, especially for imbalanced datasets.”

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✍️ Resume Tip for This Role

Include a section titled:

🔧 Projects & Tools

• Customer Churn Prediction | Python, Scikit-learn, SMOTE
• Sentiment Analysis on Tweets | NLTK, pandas, logistic regression
• Movie Recommender System | Python, Cosine Similarity, Flask

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📩 Application Steps

1. Visit official Honeywell careers page.
2. Search for "Data Scientist – Freshers/Entry Level" role

1. Submit updated resume with focus on:

• Data science projects
• GitHub/Kaggle links
• Python/R experience
• Business value from insights

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Apply to Honeywell hiring for Data Scientist jobs