Data Scientist - R01571772

Brillio 2; Pune District; On-site; INR 1,500,000 - 3,000,000; Full time

Job summary

Brillio is seeking a Data Scientist with 4+ years of experience in advanced data science, statistical analysis, and machine learning model development. You will work on large datasets, build production-ready models, and collaborate with cross-functional teams to translate business needs into analytical solutions.

The role requires strong Python and PySpark skills, experience with regression and classification, and deployment of models in cloud-native environments using tools like KubeFlow or

Qualifications

• Must have 4+ years in advanced data science, statistical analysis, and ML model development.
• Experience with large datasets and production model deployment.
• Hands-on with Python/R and ML frameworks.

Responsibilities

• Design, develop, and deploy statistics and ML models using Python, R, and frameworks.
• Conduct hypothesis tests, regressions, and probabilistic modeling for business insights.
• Implement data quality checks with Great Expectations and Evidently AI.
• Collaborate with cross-functional teams to translate business requirements into analytical solutions.
• Develop, optimize, and maintain forecasting models (e.g., exponential smoothing, ARIMA).
• Build, train, and evaluate classification and regression models (TensorFlow, PyTorch, scikit-learn, Keras).
• Deploy and monitor models in production with cloud-native tools (KubeFlow, BentoML).
• Document methodologies and contribute to analytics best practices.

Skills

Python

PySpark

Statistics

Machine Learning

Hypothesis Testing

Regression

Classification

Production ML

Cloud Deployment

Model Monitoring

Education

Bachelors in CS/Math/Data Science

Data Science Certification

Tools

Great Expectations

Evidently AI

KubeFlow

BentoML

TensorFlow

PyTorch

Job description

Data Scientist

Experience Range:

With at least 4 years of experience in advanced data science, statistical analysis, and machine learning model development, including hands-on work with large datasets and production model deployment.

Key Responsibilities:

• Design, develop, and deploy advanced statistical and machine learning models using Python, R, and specialized frameworks to address complex business challenges
• Conduct rigorous statistical analysis, including hypothesis testing, regression analysis, and probabilistic modeling, to extract actionable insights from large-scale data
• Implement and validate data quality checks using tools such as Great Expectations and Evidently AI to ensure data and model integrity
• Collaborate with cross-functional teams to define data-driven strategies, translate business requirements into analytical solutions, and present findings to stakeholders
• Develop, optimize, and maintain forecasting models using techniques such as exponential smoothing, ARIMA, and ARIMAX to support business planning
• Build, train, and evaluate classification and regression models using ML frameworks (TensorFlow, PyTorch, Sci-Kit Learn, Keras, MXNet, CNTK)
• Deploy and monitor models in production environments using scalable cloud-native tools such as KubeFlow and BentoML
• Document methodologies and contribute to continuous improvement of analytics best practices

Required Skills:

• Advanced proficiency in Python and PySpark for data analysis and model development
• Expertise in statistical analysis and computing using SAS or SPSS
• Hands-on experience with regression techniques including linear and logistic regression
• Strong knowledge of hypothesis testing, including T-Test and Z-Test methodologies
• Proficient in building and interpreting probabilistic graphical models
• Experience with classification algorithms such as Decision Trees and Support Vector Machines (SVM)
• Skilled in forecasting techniques including exponential smoothing, ARIMA, and ARIMAX
• Familiarity with distance metrics such as Hamming, Euclidean, and Manhattan Distance
• Working knowledge of R and R Studio for statistical modeling
• Experience with data validation and monitoring tools such as Great Expectations and Evidently AI

Preferred Skills:

• Experience deploying machine learning models using KubeFlow or BentoML
• Proficiency with deep learning frameworks such as TensorFlow, PyTorch, Keras, MXNet, or CNTK
• Background in cloud-based analytics platforms (e.g., AWS SageMaker, Azure ML, Google AI Platform)
• Exposure to automated machine learning (AutoML) workflows

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 provider (e.g., Microsoft Certified: Azure Data Scientist Associate, IBM Data Science Professional Certificate)