Data Scientist – R01571772

Brillio; Pune District; On-site; INR 2,800,000 - 4,200,000; Full time

Job summary

Brillio seeks an experienced Data Scientist in Pune with 4+ years of hands-on data science, ML model development, and production deployment across large datasets. You will design and deploy models using Python, R, and frameworks, and collaborate with cross-functional teams to translate business needs into analytics solutions.

The role emphasizes strong statistical analysis, data quality validation, and forecasting model development with tools like TensorFlow, PyTorch, and ARIMA.

Qualifications

• 4+ years in advanced data science, ML model development, and production deployment.
• Experience with large datasets and production ML workflows.
• Proficient in Python and PySpark for data analysis and modeling.
• Strong statistical analysis and hypothesis testing skills.
• Experience with model validation and data quality checks.

Responsibilities

• Design, develop, and deploy ML models using Python, R, and frameworks.
• Conduct rigorous statistical analysis to extract insights from large datasets.
• Implement data quality checks with Great Expectations and Evidently AI.
• Collaborate with cross-functional teams to translate business needs into analytical solutions.
• Develop and maintain forecasting models (exponential smoothing, ARIMA, ARIMAX).
• Build, train, and evaluate classification and regression models (TensorFlow, PyTorch, Scikit-Learn, Keras).
• Deploy and monitor models in production using cloud-native tools (KubeFlow, BentoML).
• Document methodologies and contribute to analytics best practices.

Skills

Python

PySpark

SAS/SPSS

Regression analysis

Hypothesis testing

Probabilistic graphical models

Classification (DT/SVM)

Forecasting (Exponential smoothing/AR)

R/R Studio

Data validation

Education

Bachelors in CS/Statistics/Data Science

Data Science or ML certification

Tools

TensorFlow

PyTorch

Scikit-Learn

Keras

MXNet

CNTK

KubeFlow

BentoML

Great Expectations

Evidently AI

Job description

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)