Data Scientist - R01570831

Brillio 2; Bengaluru; On-site; INR 1,500,000 - 2,300,000; Full time

Job summary

Brillio is seeking a Data Scientist with 4–6 years of hands-on experience in ML and statistical modeling to drive data-driven decisions.

You will develop and deploy predictive models using Python, PySpark, R, and SAS; apply forecasting, regression, and classification techniques; and collaborate with cross-functional teams to translate business goals into actionable insights.

A strong foundation in analytics, excellent problem-solving, and clear communication are essential for success in this role.

Qualifications

• Bachelor's degree or higher in a quantitative field is required.
• Certifications in ML/DS from recognized platforms are preferred.
• SAS Certified Statistical Business Analyst or similar is beneficial.

Responsibilities

• Develop and implement Next Best Offer models and advanced data science solutions.
• Apply statistical techniques (hypothesis testing, t-tests, z-tests, regression) to extract insights.
• Build, validate, and optimize ML models using Python, PySpark, and R.
• Use probabilistic graph models and classifiers (decision trees, SVM) for business problems.
• Optimize ML pipelines for scalable deployment with KubeFlow and BentoML.
• Conduct statistical analyses with SAS, SPSS, and R Studio to guide strategies.
• Monitor model performance with evaluation metrics and improve as needed.
• Collaborate with cross-functional teams to translate business needs into data solutions.

Skills

Python

PySpark

SAS

SPSS

R

Probabilistic graph models

Regression methods

Forecasting methods

TensorFlow

PyTorch

Scikit-learn

CNTK

Keras

MXNet

Decision trees

SVM

Distance metrics

KubeFlow

BentoML

Education

Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a quantitative discipline

Certification in Machine Learning or Data Science (Coursera, edX, DataCamp)

Certification in statistical analysis or analytics (SAS Certified)

Job description

Data Scientist

Experience Range: With 4 to 6 years of experience in advanced data science roles, including hands‑on involvement in machine learning and statistical modeling projects

Key Responsibilities:

• Develop and implement Next Best Offer models and advanced data science solutions to drive business objectives and enhance customer engagement
• Apply statistical techniques such as hypothesis testing, t-tests, z-tests, and regression methods to extract actionable insights and support data-driven decision-making
• Build, validate, and optimize machine learning models using Python, PySpark, and R to ensure high accuracy and reliability
• Leverage probabilistic graph models and classification algorithms, including decision trees and support vector machines, to address complex business challenges
• Optimize and automate machine learning pipelines for scalable deployment using KubeFlow and BentoML, improving operational efficiency
• Conduct comprehensive statistical analysis with SAS, SPSS, and R Studio to inform business strategies
• Monitor model performance using evaluation metrics and recommend data-driven improvements to maintain model effectiveness
• Collaborate with cross-functional teams to translate business requirements into impactful data science solutions

Required Skills:

• Python
• PySpark
• SAS
• SPSS
• R
• Probabilistic graph models
• Regression methods (linear and logistic)
• Forecasting methods (exponential smoothing, ARIMA, ARIMAX)
• TensorFlow
• PyTorch
• Scikit-learn
• CNTK
• Keras
• MXNet
• Decision trees
• Support Vector Machines (SVM)
• Distance metrics (Hamming, Euclidean, Manhattan)
• KubeFlow
• BentoML

Preferred Skills:

• Experience with Great Expectations and Evidently AI for model validation and monitoring
• Expertise in deploying machine learning models in cloud-based environments
• Knowledge of advanced ensemble methods and boosting algorithms
• Familiarity with A/B testing and experimental design
• Background in recommendation systems and personalization algorithms

Desired Qualifications:

• Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a quantitative discipline relevant to data science
• Certification in Machine Learning or Data Science from a recognized institution such as Coursera, edX, or DataCamp
• Relevant certification in statistical analysis or analytics, such as SAS Certified Statistical Business Analyst