Senior Data Science Lead - R01571450

Brillio 2; Bengaluru; On-site; INR 3,000,000 - 6,000,000; Full time

Job summary

Brillio 2 seeks a Senior Data Science Lead to drive advanced analytics initiatives in Bengaluru. You will design and deploy complex statistical models, forecasts, and ML solutions that address strategic business challenges.

You will lead a team, mentor colleagues, and collaborate with cross-functional partners to ensure robust data-driven decision making. Strong communication and stakeholder alignment are essential.

Qualifications

• 8+ years of experience in data science, statistics, and analytics with leadership
• Experience leading advanced data science initiatives
• Strong statistical modeling and forecasting capabilities
• Proficiency in Python, R, and ML frameworks

Responsibilities

• Lead design and implementation of advanced statistical models and ML algorithms to address business challenges
• Develop, validate, and optimize predictive and forecasting models to improve accuracy
• Conduct hypothesis testing (T-Tests, Z-Tests) to inform experiments
• Collaborate with cross-functional teams to define project requirements and deliver data-driven solutions
• Oversee data preprocessing, feature engineering, and data quality assessments using Great Expectations and Evidently AI
• Mentor team members in Python, PySpark, R, TensorFlow, PyTorch, Scikit-Learn
• Implement end-to-end data science workflows and model deployment pipelines with KubeFlow and BentoML
• Evaluate model results, communicating findings with statistical rigor to stakeholders

Skills

Python

PySpark

R

SAS

TST/HT

ML Frameworks

ARIMA/ARIMAX

Time Series

Statistics

MLOps

Cloud Platforms

Education

Bachelor’s degree in CS/Stats/Data Science

Tools

Great Expectations

Evidently AI

KubeFlow

BentoML

CNTK

Job description

Senior Data Science Lead

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)