Sr. Data Scientist

Full Time

Bengaluru

Posted 4 weeks ago

Statistical & Analytical Foundations
1. Probability theory and distributions
2. Hypothesis testing and confidence intervals
3. Regression techniques (linear, logistic, regularized)
4. Model evaluation metrics and statistical validation
5. Experimental design and A/B testing fundamentals
Machine Learning Core
1. Supervised learning (classification, regression)
2. Unsupervised learning (clustering, dimensionality reduction)
3. Time series analysis and forecasting
4. Model selection based on problem context
5. Bias–variance trade-off understanding
End-to-End Model Lifecycle
1. Business problem framing → ML formulation
2. Exploratory Data Analysis (EDA)
3. Feature engineering and selection
4. Model training, validation, and tuning
5. Performance measurement and error analysis
Programming & Data Handling
1. Strong proficiency in Python or R
2. Hands-on experience with ML libraries (scikit-learn, statsmodels, etc.)
3. Data manipulation using pandas / NumPy
4. Writing reproducible and modular code
Business Translation
1. Converting business requirements into analytical solutions
2. Delivering actionable insights, not just models
3. Clear explanation of model outcomes and trade-offs

Model Deployment & Production Exposure
1. Experience deploying models into production environments
2. Integration of ML models with business systems or APIs
3. Understanding of inference pipelines and latency constraints
Advanced Machine Learning
1. Ensemble techniques (bagging, boosting, stacking)
2. Advanced feature engineering techniques
3. Handling imbalanced data and rare-event modelling
4. Hyperparameter optimization strategies
Experimentation & Optimization
1. Designing and analyzing A/B tests
2. Domain Expertise
3. Interpreting trade-offs across multiple KPIs
Data Storytelling & Visualization
1. Building automated dashboards (Power BI, Tableau, etc.)
2. Translating statistical outputs into executive narratives
3. KPI design and monitoring
Collaboration & Communication
1. Working with cross-functional teams (Product, Engineering, Business)
2. Documentation of assumptions, models, and results

Domain Expertise
1. Industry-specific knowledge (banking, healthcare, retail, etc.)
2. Regulatory or compliance-aware modelling
Advanced Engineering & MLOps
1. Model monitoring and drift detection
2. CI/CD pipelines for ML
3. ML experiment tracking tools
Advanced Analytics & AI
1. NLP, Computer Vision, or Deep Learning
2. Large Language Models (LLMs) and GenAI use cases
Big Data & Scale
1. Distributed computing (Spark, Hadoop)
2. Working with very large or streaming datasets
Research & Innovation
1. Designing novel modelling approaches
2. Benchmarking and model experimentation
3. Publishing or internal R&D contributions

Job Category

Data Engineering