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Post #4706 4.94K
HSBC
Position: Manager - Data and Analytics
Qualifications: Bachelorโ€™s/ Masterโ€™s Degree
Experience: Freshers/ Experienced
Location: Bangalore; Kolkata, India (Hybrid)

๐Ÿ“ŒApply Now: https://portal.careers.hsbc.com/careers/job/563774610649789

Like for more job opportunities โค๏ธ
  • ๐Ÿ‘ 3
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Post #4704 5.12K
๐—™๐—ฅ๐—˜๐—˜ ๐—”๐—œ & ๐— ๐—ฎ๐—ฐ๐—ต๐—ถ๐—ป๐—ฒ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป๐—ถ๐—ป๐—ด ๐—ฅ๐—ฒ๐˜€๐—ผ๐˜‚๐—ฟ๐—ฐ๐—ฒ๐˜€ | ๐Ÿฐ ๐—•๐—ฒ๐˜€๐˜ ๐—ฌ๐—ผ๐˜‚๐—ง๐˜‚๐—ฏ๐—ฒ ๐—–๐—ต๐—ฎ๐—ป๐—ป๐—ฒ๐—น๐˜€ ๐Ÿš€

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Post #4703 4.5K
Business, Sales & Growth Jobs

โ€ข Databricks is hiring Pre-sales Senior Technical Solutions Engineer
โ€ข Anthropic is hiring Enterprise Account Executive
โ€ข Zscaler is hiring Account Executive
โ€ข Fireblocks is hiring Compliance Owner
โ€ข DoorDash USA is hiring Business Development Lead
โ€ข Twilio is hiring Partner Program Manager
โ€ข OpenAI is hiring Device Safety & Risk Operations Specialist
โ€ข Block is hiring Product Accounting Manager
โ€ข Asana is hiring Manager
โ€ข Airbnb is hiring Senior Platform Manager

Like for more job opportunities
  • โค 3
Post #4701 4.78K
๐Ÿ’ป ๐— ๐—ฎ๐˜€๐˜๐—ฒ๐—ฟ ๐—ฆ๐—ค๐—Ÿ ๐—™๐—ข๐—ฅ ๐—™๐—ฅ๐—˜๐—˜ | ๐Ÿฑ ๐—”๐—บ๐—ฎ๐˜‡๐—ถ๐—ป๐—ด ๐—ช๐—ฒ๐—ฏ๐˜€๐—ถ๐˜๐—ฒ๐˜€ ๐—ง๐—ผ ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—ฆ๐—ค๐—Ÿ ๐Ÿš€

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  • โค 1
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Post #4699 4.77K
๐Ÿš€ ๐—ก๐—ฉ๐—œ๐——๐—œ๐—” ๐—™๐—ฅ๐—˜๐—˜ ๐—”๐—œ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ | ๐—Ÿ๐—ฒ๐—ฎ๐—ฟ๐—ป ๐—™๐—ฟ๐—ผ๐—บ ๐—”๐—œ ๐—œ๐—ป๐—ฑ๐˜‚๐˜€๐˜๐—ฟ๐˜† ๐—Ÿ๐—ฒ๐—ฎ๐—ฑ๐—ฒ๐—ฟ๐˜€

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  • โค 2
Post #4698 4.58K
โœ… Data Science Interview Prep Guide

1๏ธโƒฃ Core Data Science Concepts
โ€ข What is Data Science vs Data Analytics vs ML
โ€ข Descriptive, diagnostic, predictive, prescriptive analytics
โ€ข Structured vs unstructured data
โ€ข Data-driven decision making
โ€ข Business problem framing

2๏ธโƒฃ Statistics Probability (Non-Negotiable)
โ€ข Mean, median, variance, standard deviation
โ€ข Probability distributions (normal, binomial, Poisson)
โ€ข Hypothesis testing p-values
โ€ข Confidence intervals
โ€ข Correlation vs causation
โ€ข Sampling bias

3๏ธโƒฃ Data Cleaning EDA
โ€ข Handling missing values outliers
โ€ข Data normalization scaling
โ€ข Feature engineering
โ€ข Exploratory data analysis (EDA)
โ€ข Data leakage detection
โ€ข Data quality validation

4๏ธโƒฃ Python SQL for Data Science
โ€ข Python (NumPy, Pandas)
โ€ข Data manipulation transformations
โ€ข Vectorization performance optimization
โ€ข SQL joins, CTEs, window functions
โ€ข Writing business-ready queries

5๏ธโƒฃ Machine Learning Essentials
โ€ข Supervised vs unsupervised learning
โ€ข Regression vs classification
โ€ข Model selection baseline models
โ€ข Overfitting, underfitting
โ€ข Biasโ€“variance tradeoff
โ€ข Hyperparameter tuning

6๏ธโƒฃ Model Evaluation Metrics
โ€ข Accuracy, precision, recall, F1
โ€ข ROC AUC
โ€ข Confusion matrix
โ€ข RMSE, MAE, log loss
โ€ข Metrics for imbalanced data
โ€ข Linking ML metrics to business KPIs

7๏ธโƒฃ Real-World Deployment Knowledge
โ€ข Feature stores
โ€ข Model deployment (batch vs real-time)
โ€ข Model monitoring drift
โ€ข Experiment tracking
โ€ข Data model versioning
โ€ข Model explainability (business-friendly)

8๏ธโƒฃ Must-Have Projects
โ€ข Customer churn prediction
โ€ข Fraud detection
โ€ข Sales or demand forecasting
โ€ข Recommendation system
โ€ข End-to-end ML pipeline
โ€ข Business-focused case study

9๏ธโƒฃ Common Interview Questions
โ€ข Walk me through an end-to-end DS project
โ€ข How do you choose evaluation metrics?
โ€ข How do you handle imbalanced data?
โ€ข How do you explain a model to leadership?
โ€ข How do you improve a failing model?

๐Ÿ”Ÿ Pro Tips
โœ”๏ธ Always connect answers to business impact
โœ”๏ธ Explain why, not just how
โœ”๏ธ Be clear about trade-offs
โœ”๏ธ Discuss failures learnings
โœ”๏ธ Show structured thinking

Double Tap โ™ฅ๏ธ For More
  • โค 8
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Post #4697 4.75K
๐—ง๐—–๐—ฆ ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ข๐—ป ๐——๐—ฎ๐˜๐—ฎ ๐— ๐—ฎ๐—ป๐—ฎ๐—ด๐—ฒ๐—บ๐—ฒ๐—ป๐˜ - ๐—˜๐—ป๐—ฟ๐—ผ๐—น๐—น ๐—™๐—ผ๐—ฟ ๐—™๐—ฅ๐—˜๐—˜๐Ÿ˜

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  • โค 1
Post #4695 5.32K
๐Ÿ“Š ๐—™๐—ฅ๐—˜๐—˜ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ | ๐—ก๐—ผ ๐—˜๐˜…๐—ฝ๐—ฒ๐—ฟ๐—ถ๐—ฒ๐—ป๐—ฐ๐—ฒ ๐—ก๐—ฒ๐—ฒ๐—ฑ๐—ฒ๐—ฑ! ๐Ÿš€

Want to start a career in Data Analytics but don't know where to begin?

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  • โค 2
Post #4692 5.62K
๐Ÿ“Š ๐—™๐—ฅ๐—˜๐—˜ ๐—ง๐—ฎ๐˜๐—ฎ ๐——๐—ฎ๐˜๐—ฎ ๐—”๐—ป๐—ฎ๐—น๐˜†๐˜๐—ถ๐—ฐ๐˜€ ๐—ฉ๐—ถ๐—ฟ๐˜๐˜‚๐—ฎ๐—น ๐—œ๐—ป๐˜๐—ฒ๐—ฟ๐—ป๐˜€๐—ต๐—ถ๐—ฝ | ๐—ช๐—ถ๐˜๐—ต ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ฒ ๐Ÿš€

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  • โค 3
Post #4689 5.58K
๐Ÿ“Š ๐—ฃ๐˜„๐—– ๐—ถ๐˜€ ๐—ผ๐—ณ๐—ณ๐—ฒ๐—ฟ๐—ถ๐—ป๐—ด ๐—ฎ ๐—™๐—ฅ๐—˜๐—˜ ๐—ฃ๐—ผ๐˜„๐—ฒ๐—ฟ ๐—•๐—œ ๐—–๐—ฒ๐—ฟ๐˜๐—ถ๐—ณ๐—ถ๐—ฐ๐—ฎ๐˜๐—ถ๐—ผ๐—ป ๐—ฃ๐—ฟ๐—ผ๐—ด๐—ฟ๐—ฎ๐—บ

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  • โค 2
Post #4687 5.2K
๐— ๐—ถ๐—ฐ๐—ฟ๐—ผ๐˜€๐—ผ๐—ณ๐˜ ๐Ÿญ๐Ÿฌ๐Ÿฌ+ ๐—™๐—ฅ๐—˜๐—˜ ๐—–๐—ผ๐˜‚๐—ฟ๐˜€๐—ฒ๐˜€ ๐—ณ๐—ผ๐—ฟ ๐—”๐˜‡๐˜‚๐—ฟ๐—ฒ, ๐—”๐—œ, ๐—–๐˜†๐—ฏ๐—ฒ๐—ฟ๐˜€๐—ฒ๐—ฐ๐˜‚๐—ฟ๐—ถ๐˜๐˜† & ๐— ๐—ผ๐—ฟ๐—ฒ ๐Ÿš€

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๐Ÿš€ Learn. Practice. Upskill. Get Career Ready
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