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What you’ll work on

Full posting

Design, develop, and deploy statistical and machine learning models in Python and R
Build forecasting models such as exponential smoothing, ARIMA, and ARIMAX
Run hypothesis testing, regression, and probabilistic modeling on large datasets
Deploy and monitor models in production using KubeFlow and BentoML
Implement data quality checks with Great Expectations and Evidently AI

From the employer’s posting

Design, develop, and deploy advanced statistical and machine learning models using Python, R

Develop, optimize, and maintain forecasting models using techniques such as exponential smoothing, ARIMA, and ARIMAX

Conduct rigorous statistical analysis, including hypothesis testing, regression analysis, and probabilistic modeling

Deploy and monitor models in production environments using scalable cloud-native tools such as KubeFlow and BentoML

Implement and validate data quality checks using tools such as Great Expectations and Evidently AI

What you’ll bring

All qualifications

Core experience

Expertise in statistical analysis and computing using SAS or SPSS
Experience with data validation and monitoring tools such as Great Expectations and Evidently AI
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)

Preferred experience

Experience deploying machine learning models using KubeFlow or BentoML
Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
Proficiency with deep learning frameworks such as TensorFlow, PyTorch, Keras, MXNet, or CNTK

Qualification wording

Expertise in statistical analysis and computing using SAS or SPSS

Experience with data validation and monitoring tools such as Great Expectations and Evidently AI

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)

Experience deploying machine learning models using KubeFlow or BentoML

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

Proficiency with deep learning frameworks such as TensorFlow, PyTorch, Keras, MXNet, or CNTK

Education & alternatives

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)

Tools in this posting

Python
R
SAS
AWS
Azure
SageMaker
Keras
PySpark
PyTorch
TensorFlow
Great_expectations

Source

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

Required Skills: - Advanced proficiency in Python and PySpark for data analysis and model development - Expertise in statistical analysis and computing using SAS or SPSS

- 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

- 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

- 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

- 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) Employment type

- 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

- 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)

- 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

- 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:

Posting record

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Status in our records: Live
First seen by us: Sep 24, 2026 · 24d ago
Recorded sightings: 63
Last seen by us: Oct 17, 2026

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Job description

View original posting ↗

Data Scientist

Job requirements

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)

Employment type

Employee

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Location & working pattern

Pune, Maharashtra, India

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