Data Scientist

DO:

• •Include ML algorithms and frameworks used
• •Show measurable accuracy improvements
• •Highlight deployments in production
• •Mention NLP, CV, or forecasting work

DON'T:

• •List libraries without projects
• •Ignore production-scale deployments
• •Exclude model validation metrics

DO:

• •Show dashboards and reporting automation
• •Mention exploratory data analysis
• •Highlight cohort, churn, or segmentation analyses
• •Tie insights to product or business decisions

DON'T:

• •Skip visualization tools
• •Ignore storytelling aspect of insights
• •Exclude collaboration outcomes

DO:

• •Tie ML models to revenue, retention, or risk reduction
• •Mention collaboration with product/finance/marketing
• •Highlight cross-functional teamwork
• •Showcase real-world deployments

DON'T:

• •Focus only on accuracy metrics
• •Exclude business adoption outcomes
• •Ignore domain expertise (fintech, retail, etc.)

Performance Metrics:

• •Improved loan approval accuracy by 26%
• •Reduced default rates by 14%
• •Forecasting reduced stockouts by 23%

Scale Metrics:

• •Deployed fraud detection for 350+ stores
• •Led team of 4 delivering segmentation framework
• •Built recommendation engine impacting 1M+ users

Business Metrics:

• •Boosted ROI of campaigns by 40% via segmentation
• •Increased conversions by 15% via recommendations
• •Delivered $12L incremental sales through analytics

ML & AI:

Predictive Modeling, NLP, Time Series Forecasting, Recommender Systems, ML Ops

Libraries:

Scikit-learn, TensorFlow, PyTorch, XGBoost, Pandas, NumPy

Tools:

Python, SQL, R, Spark, Tableau, Power BI