Senior Data Science Lead - R01571450
Posted Updated
Summary
Senior data science leadership role in Bangalore: design and implement statistical and machine learning models (forecasting with ARIMA/ARIMAX, hypothesis testing), run end-to-end ML workflows and deployment pipelines with KubeFlow and BentoML, and mentor team members using Python, PySpark, R, and frameworks like TensorFlow and PyTorch.
Senior Data Science Lead
Job requirements
Experience Range: With at least 8 years of experience in data science, statistical modeling, and advanced analytics, including up to 12 years leading advanced data science initiatives Key Responsibilities:
- Lead the design and implementation of advanced statistical models and machine learning algorithms to address complex business challenges and deliver actionable insights
- Develop, validate, and optimize predictive and forecasting models using techniques such as exponential smoothing, ARIMA, and ARIMAX to improve business forecasting accuracy
- Conduct rigorous hypothesis testing, including T-Tests and Z-Tests, to inform experimental design and support data-driven decision making
- Collaborate with cross-functional teams to define project requirements, ensure alignment with organizational objectives, and deliver impactful data science solutions
- Oversee data preprocessing, feature engineering, and data quality assessments utilizing tools such as Great Expectations and Evidently AI
- Mentor and guide team members in the use of Python, PySpark, R, and machine learning frameworks including TensorFlow, PyTorch, and Sci-Kit Learn
- Implement and manage end-to-end data science workflows and model deployment pipelines using KubeFlow and BentoML
- Evaluate and interpret model results, ensuring statistical rigor and effectively communicating findings to stakeholders
Required Skills:
- Python and PySpark for data analysis and model development
- Statistical analysis and computing using SAS or SPSS
- Hypothesis testing methodologies including T-Test and Z-Test
- Regression techniques such as linear and logistic regression
- Development and deployment of machine learning models using TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet
- Probabilistic graph models and classification algorithms including decision trees and SVM
- Time series forecasting methods including exponential smoothing, ARIMA, and ARIMAX
- Distance metrics such as Hamming, Euclidean, and Manhattan distances
- R and R Studio for statistical computing and visualization
- Data validation and monitoring tools including Great Expectations and Evidently AI
Preferred Skills:
- Advanced model interpretability and explainability techniques
- Experience with cloud-based data science platforms such as AWS SageMaker, Azure ML, or Google AI Platform
- Expertise in MLOps best practices for scalable model deployment
- Design and implementation of deep learning architectures for structured and unstructured data
Desired Qualifications:
- Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
- Certification in Data Science or Machine Learning from a recognized institution (such as Certified Data Scientist or TensorFlow Developer Certificate)
- Relevant certification in statistical analysis tools or platforms (such as SAS Certified Advanced Analytics Professional or Microsoft Certified: Azure Data Scientist Associate)
Skills
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