DepartmentAI & Data Engineering, AI & Data Engineering : Data Science
Company size5,001–10,000 people
PostedSep 24, 2026 · 2w ago
Verified live11h ago
At a glanceSummarised by Seekless from the posting.
Must have13
At least 4 years of experience in advanced data science, statistical analysis, and machine learning model development
Hands-on work with large datasets and production model deployment
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
Bachelor's degree in Computer Science, Statistics, Mathematics, Data Science, or a closely related discipline
Nice to have4
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
Skills
Python
R
PySpark
SAS
SPSS
TensorFlow
PyTorch
Sci-Kit Learn
Keras
MXNet
CNTK
Great Expectations
Evidently AI
KubeFlow
BentoML
AWS SageMaker
Azure ML
Google AI Platform
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:
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)