Pay: Salary not listed in the saved posting
Work setup: Hybrid — work setup source
Work setup in the employer’s listing: hybrid
From the employer’s posting
Employment: Unconfirmed

What you’ll work on

Design and implement statistical models for business insights
Develop machine learning models for classification and prediction
Build and deploy scalable models into production environments
Apply time series forecasting methods to trends and outcomes
Present data findings and recommendations to stakeholders

From the employer’s posting

Design and implement robust statistical models using advanced hypothesis testing, regression, and forecasting techniques

Develop and optimize machine learning algorithms for classification, prediction, and probabilistic graph models

Build, train, and deploy scalable models using ML frameworks

Apply advanced time series forecasting methods, including exponential smoothing, ARIMA, and ARIMAX

Present complex data findings to stakeholders, translating insights into actionable recommendations

What you’ll bring

All qualifications

Core experience

At least 4 years of hands-on data science experience
Expert regression analysis (linear and logistic)
Proficiency in Python and PySpark
Experience with SAS and SPSS statistical analysis
Skill in R and R Studio for statistics and visualization

Preferred experience

Practical experience with Great Expectations and Evidently AI for advanced data validation
Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
Proficiency in cloud-based model deployment tools such as KubeFlow and BentoML
Expertise in feature engineering and model interpretability techniques

Qualification wording

With at least 4 years of hands-on experience in advanced data science

Expert-level regression analysis (linear and logistic) for predictive modeling

Proficient programming in Python and PySpark for data manipulation and model development

Extensive experience with statistical analysis using SAS and SPSS

Skilled in R and R Studio for statistical analysis and visualization

Practical experience with Great Expectations and Evidently AI for advanced data validation

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

Proficiency in cloud-based model deployment tools such as KubeFlow and BentoML

Expertise in feature engineering and model interpretability techniques

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 institution, such as Microsoft Certified: Azure Data Scientist Associate or TensorFlow Developer Certificate

Tools in this posting

Python
R
SAS
AWS
Azure
SageMaker
Keras
PySpark
PyTorch
TensorFlow
Google Cloud (GCP)
Great_expectations

Source

- Design and implement robust statistical models using advanced hypothesis testing, regression, and forecasting techniques to deliver actionable business insights - Develop and optimize machine learning algorithms for classification, prediction, and probabilistic graph models utilizing Python, PySpark, and R - Conduct comprehensive statistical analysis with SAS, SPSS, and R Studio to support data-driven decision-making

- Expert-level regression analysis (linear and logistic) for predictive modeling - Proficient programming in Python and PySpark for data manipulation and model development - Extensive experience with statistical analysis using SAS and SPSS

- Develop and optimize machine learning algorithms for classification, prediction, and probabilistic graph models utilizing Python, PySpark, and R - Conduct comprehensive statistical analysis with SAS, SPSS, and R Studio to support data-driven decision-making - Build, train, and deploy scalable models using ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet

- Calculation and application of distance metrics (Hamming, Euclidean, Manhattan) - Skilled in R and R Studio for statistical analysis and visualization Preferred Skills:

- Proficient programming in Python and PySpark for data manipulation and model development - Extensive experience with statistical analysis using SAS and SPSS - Hands-on expertise in probabilistic graph models for complex data relationships

- Expertise in feature engineering and model interpretability techniques - Familiarity with cloud-based data science platforms such as AWS SageMaker, Azure ML, or Google Cloud AI Platform 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 Microsoft Certified: Azure Data Scientist Associate or TensorFlow Developer Certificate Employment type

- Conduct comprehensive statistical analysis with SAS, SPSS, and R Studio to support data-driven decision-making - Build, train, and deploy scalable models using ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet - Apply advanced time series forecasting methods, including exponential smoothing, ARIMA, and ARIMAX, to analyze trends and predict outcomes

- Implementation of classification algorithms such as decision trees and support vector machines (SVM) - Deep familiarity with ML frameworks: TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet - Calculation and application of distance metrics (Hamming, Euclidean, Manhattan)

- Streamline model deployment and lifecycle management in production environments using KubeFlow and BentoML - Implement and validate data quality checks with Great Expectations and Evidently AI to ensure dataset integrity - Present complex data findings to stakeholders, translating insights into actionable recommendations that drive business outcomes

Preferred Skills: - Practical experience with Great Expectations and Evidently AI for advanced data validation - Proficiency in cloud-based model deployment tools such as KubeFlow and BentoML

Posting record

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Status in our records: Live
First seen by us: Sep 30, 2026 · 18d ago
Recorded sightings: 53
Last seen by us: Oct 17, 2026

These dates show when we found the listing. Check the employer’s website to confirm it is still accepting applications.

Job description

View original posting ↗

Data Scientist

Job requirements

Experience Range: With at least 4 years of hands-on experience in advanced data science, including statistical analysis and machine learning, and up to 6 years in similar roles Key Responsibilities:

Design and implement robust statistical models using advanced hypothesis testing, regression, and forecasting techniques to deliver actionable business insights
Develop and optimize machine learning algorithms for classification, prediction, and probabilistic graph models utilizing Python, PySpark, and R
Conduct comprehensive statistical analysis with SAS, SPSS, and R Studio to support data-driven decision-making
Build, train, and deploy scalable models using ML frameworks such as TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, and MXNet
Apply advanced time series forecasting methods, including exponential smoothing, ARIMA, and ARIMAX, to analyze trends and predict outcomes
Streamline model deployment and lifecycle management in production environments using KubeFlow and BentoML
Implement and validate data quality checks with Great Expectations and Evidently AI to ensure dataset integrity
Present complex data findings to stakeholders, translating insights into actionable recommendations that drive business outcomes

Required Skills:

Advanced application of hypothesis testing methodologies, including T-Test and Z-Test
Expert-level regression analysis (linear and logistic) for predictive modeling
Proficient programming in Python and PySpark for data manipulation and model development
Extensive experience with statistical analysis using SAS and SPSS
Hands-on expertise in probabilistic graph models for complex data relationships
Mastery of time series forecasting techniques (exponential smoothing, ARIMA, ARIMAX)
Implementation of classification algorithms such as decision trees and support vector machines (SVM)
Deep familiarity with ML frameworks: TensorFlow, PyTorch, Sci-Kit Learn, CNTK, Keras, MXNet
Calculation and application of distance metrics (Hamming, Euclidean, Manhattan)
Skilled in R and R Studio for statistical analysis and visualization

Preferred Skills:

Practical experience with Great Expectations and Evidently AI for advanced data validation
Proficiency in cloud-based model deployment tools such as KubeFlow and BentoML
Background in large-scale data processing and distributed computing environments
Expertise in feature engineering and model interpretability techniques
Familiarity with cloud-based data science platforms such as AWS SageMaker, Azure ML, or Google Cloud AI Platform

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 Microsoft Certified: Azure Data Scientist Associate or TensorFlow Developer Certificate

Employment type

Employee

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Source details

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Pay

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Location & working pattern

Pune, Maharashtra, India

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