Data Scientist - R01570830
Summary
Mid-level data scientist (4-6 years' experience) in Bangalore building Next Best Offer models and ML solutions using Python, PySpark, R, and frameworks like TensorFlow, KubeFlow, and BentoML. Day to day involves statistical analysis (hypothesis testing, regression, forecasting), model building/validation, and pipeline automation to support business decisions.
Job requirements
- Develop and implement Next Best Offer models and advanced data science solutions to drive business objectives and enhance customer engagement
- Apply statistical techniques such as hypothesis testing, t-tests, z-tests, and regression methods to extract actionable insights and support data-driven decision-making
- Build, validate, and optimize machine learning models using Python, PySpark, and R to ensure high accuracy and reliability
- Leverage probabilistic graph models and classification algorithms, including decision trees and support vector machines, to address complex business challenges
- Optimize and automate machine learning pipelines for scalable deployment using KubeFlow and BentoML, improving operational efficiency
- Conduct comprehensive statistical analysis with SAS, SPSS, and R Studio to inform business strategies
- Monitor model performance using evaluation metrics and recommend data-driven improvements to maintain model effectiveness
- Collaborate with cross-functional teams to translate business requirements into impactful data science solutions
- Python
- PySpark
- SAS
- SPSS
- R
- Probabilistic graph models
- Regression methods (linear and logistic)
- Forecasting methods (exponential smoothing, ARIMA, ARIMAX)
- TensorFlow
- PyTorch
- Scikit-learn
- CNTK
- Keras
- MXNet
- Decision trees
- Support Vector Machines (SVM)
- Distance metrics (Hamming, Euclidean, Manhattan)
- KubeFlow
- BentoML
- Experience with Great Expectations and Evidently AI for model validation and monitoring
- Expertise in deploying machine learning models in cloud-based environments
- Knowledge of advanced ensemble methods and boosting algorithms
- Familiarity with A/B testing and experimental design
- Background in recommendation systems and personalization algorithms
- Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a quantitative discipline relevant to data science
- Certification in Machine Learning or Data Science from a recognized institution such as Coursera, edX, or DataCamp
- Relevant certification in statistical analysis or analytics, such as SAS Certified Statistical Business Analyst
Skills
As published by lever
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