Senior Data Science Lead - R01571450

Brillio; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Brillio seeks a Senior Data Science Lead to guide end-to-end data science initiatives, architecting advanced statistical models and forecasting systems. The role emphasizes robust hypothesis testing, cross-functional collaboration, and mentoring a high-performing team across Python, PySpark, R, and ML frameworks.

The candidate will drive model deployment pipelines and ensure rigorous interpretation of results for strategic decisions within a fast-paced environment in Bengaluru, India.

Qualifications

• At least 8 years in data science and analytics, with up to 12 years leading initiatives.
• Strong expertise in statistical modeling and machine learning.
• Experience delivering end-to-end data science projects.

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

Skills

Python & PySpark

Statistical modeling

Time series forecasting

Hypothesis testing

TensorFlow/PyTorch

R & R Studio

ML deployment (MLOps)

Big data tooling (Spark)

Communication to stakeholders

Education

Bachelor's in CS/Math/Stats

Data Science Certification

Tools

SAS/ SPSS

Job description

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)