Sr. Data Scientist

Overview

Looking for Immediate Joiners

Location – Bengaluru / Chennai

Work Model : Hybrid / Remote

• Primary Skills (Must-Have | Non-Negotiable)
• Statistical & Analytical Foundations
• Probability theory and distributions
• Hypothesis testing and confidence intervals
• Regression techniques (linear, logistic, regularized)
• Model evaluation metrics and statistical validation
• Experimental design and A/B testing fundamentals
• Machine Learning Core
• Supervised learning (classification, regression)
• Unsupervised learning (clustering, dimensionality reduction)
• Time series analysis and forecasting
• Model selection based on problem context
• Bias–variance trade-off understanding
• End-to-End Model Lifecycle
• Business problem framing → ML formulation
• Exploratory Data Analysis (EDA)
• Feature engineering and selection
• Model training, validation, and tuning
• Performance measurement and error analysis
• Programming & Data Handling
• Strong proficiency in Python or R
• Hands-on experience with ML libraries (scikit-learn, statsmodels, etc.)
• Data manipulation using pandas / NumPy
• Writing reproducible and modular code
• Business Translation
• Converting business requirements into analytical solutions
• Delivering actionable insights, not just models
• Clear explanation of model outcomes and trade-offs
• Secondary Skills (Good-to-Have | Strong Differentiators)
• Model Deployment & Production Exposure
• Experience deploying models into production environments
• Integration of ML models with business systems or APIs
• Understanding of inference pipelines and latency constraints
• Advanced Machine Learning
• Ensemble techniques (bagging, boosting, stacking)
• Advanced feature engineering techniques
• Handling imbalanced data and rare-event modelling
• Hyperparameter optimization strategies
• Experimentation & Optimization
• Designing and analyzing A/B tests
• Domain Expertise
• Interpreting trade-offs across multiple KPIs
• Data Storytelling & Visualization
• Building automated dashboards (Power BI, Tableau, etc.)
• Translating statistical outputs into executive narratives
• KPI design and monitoring
• Collaboration & Communication
• Working with cross-functional teams (Product, Engineering, Business)
• Documentation of assumptions, models, and results
• Optional Skills (Nice-to-Have | Role-Dependent)
• Domain Expertise
• Industry-specific knowledge (banking, healthcare, retail, etc.)
• Regulatory or compliance-aware modelling
• Advanced Engineering & MLOps
• Model monitoring and drift detection
• CI/CD pipelines for ML
• ML experiment tracking tools
• Advanced Analytics & AI
• NLP, Computer Vision, or Deep Learning
• Large Language Models (LLMs) and GenAI use cases
• Big Data & Scale
• Distributed computing (Spark, Hadoop)
• Working with very large or streaming datasets
• Research & Innovation
• Designing novel modelling approaches
• Benchmarking and model experimentation
• Publishing or internal R&D contributions