Senior Data Scientist - Artificial Intelligence
IBM
Senior Data Scientist - Artificial Intelligence
Job Description
Senior Data Scientist - Artificial Intelligence
📋 Job Overview
As a Senior Data Scientist at IBM Consulting, you will lead complex AI initiatives across various industries, providing technical leadership and shaping client AI strategies. You will drive innovation, collaborate with diverse teams, and develop advanced machine learning solutions to deliver measurable business impact.
📍 Location: Multiple Locations (Remote/Hybrid)
💼 Career Level: Professional
🎯 Key Responsibilities
- Lead cross-functional collaboration with business stakeholders, data engineers, and technical teams, driving the definition of data-driven problems and opportunities
- Provide technical leadership, promoting quality standards, code reviews, experimentation, and best practices across the team
- Oversee complex data quality investigations and model performance issues, ensuring scalability, robustness, and long-term maintainability
- Design, develop, and validate advanced machine learning solutions, including supervised, unsupervised, and deep learning approaches that deliver measurable business impact
- Coordinate and optimize data preprocessing, feature engineering, and in-depth exploratory data analysis, establishing efficient and reproducible workflows
- Produce and review high-quality technical documentation, ensuring alignment with data architectures, internal standards, and compliance requirements
✅ Required Qualifications
- Minimum 6 years of professional experience as a Data Scientist, with demonstrated ownership of end-to-end AI/ML projects in enterprise environments
- Academic background in data science, statistics, computer science, or related disciplines
- Strong command of machine learning, advanced statistical methods, model evaluation, and optimization techniques
- Proven ability to lead technical teams and projects, identifying bottlenecks, defining priorities, and ensuring delivery excellence
- Strong understanding of data modeling concepts and relational/NoSQL database principles
- Advanced proficiency in Python and SQL, including ML frameworks such as scikit-learn, PyTorch, or TensorFlow
- Hands-on experience deploying models into production, including MLOps practices (CI/CD, monitoring, drift management, reproducibility)
⭐ Preferred Qualifications
- Master’s degree or advanced specialization
- Interest in earning advanced cloud or ML certifications (AWS/GCP/Azure), or possession of relevant certifications already
- Agile mindset, adaptability, initiative, and strong critical thinking
- In-depth understanding of data governance principles and data lifecycle management
- Significant experience with advanced data visualization and data storytelling, supporting executive-level decision-making
- Familiarity with cloud-based ML pipelines, distributed training, and scalable deployment technologies
- Advanced knowledge of statistical analysis, complex ML algorithms, and large-scale data processing techniques
🛠️ Required Skills
- Machine learning
- Advanced statistical methods
- Model evaluation
- Optimization techniques
- Data modeling
- Relational databases
- NoSQL databases
- Python
- SQL
- scikit-learn
- PyTorch
- TensorFlow
- MLOps
- CI/CD
- Monitoring
- Drift management
- Reproducibility
- Data governance
- Data lifecycle management
- Data visualization
- Data storytelling
- Cloud-based ML pipelines
- Distributed training
- Scalable deployment
- Statistical analysis
- Complex ML algorithms
- Large-scale data processing
- Agile mindset
- Adaptability
- Initiative
- Critical thinking
- Leadership
- Collaboration
🎁 Benefits & Perks
- Opportunity to learn and develop yourself and your career
- Encouragement to be courageous and experiment every day
- Continuous trust and support in an environment where everyone can thrive
- Flexible working patterns
- Equal-opportunity employment
Locations
- Multiple Locations, India (Remote)
Salary
Estimated Salary Rangemedium confidence
2,500,000 - 4,200,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine learningintermediate
- Advanced statistical methodsintermediate
- Model evaluationintermediate
- Optimization techniquesintermediate
- Data modelingintermediate
- Relational databasesintermediate
- NoSQL databasesintermediate
- Pythonintermediate
- SQLintermediate
- scikit-learnintermediate
- PyTorchintermediate
- TensorFlowintermediate
- MLOpsintermediate
- CI/CDintermediate
- Monitoringintermediate
- Drift managementintermediate
- Reproducibilityintermediate
- Data governanceintermediate
- Data lifecycle managementintermediate
- Data visualizationintermediate
- Data storytellingintermediate
- Cloud-based ML pipelinesintermediate
- Distributed trainingintermediate
- Scalable deploymentintermediate
- Statistical analysisintermediate
- Complex ML algorithmsintermediate
- Large-scale data processingintermediate
- Agile mindsetintermediate
- Adaptabilityintermediate
- Initiativeintermediate
- Critical thinkingintermediate
- Leadershipintermediate
- Collaborationintermediate
Required Qualifications
- Minimum 6 years of professional experience as a Data Scientist, with demonstrated ownership of end-to-end AI/ML projects in enterprise environments (experience)
- Academic background in data science, statistics, computer science, or related disciplines (experience)
- Strong command of machine learning, advanced statistical methods, model evaluation, and optimization techniques (experience)
- Proven ability to lead technical teams and projects, identifying bottlenecks, defining priorities, and ensuring delivery excellence (experience)
- Strong understanding of data modeling concepts and relational/NoSQL database principles (experience)
- Advanced proficiency in Python and SQL, including ML frameworks such as scikit-learn, PyTorch, or TensorFlow (experience)
- Hands-on experience deploying models into production, including MLOps practices (CI/CD, monitoring, drift management, reproducibility) (experience)
Preferred Qualifications
- Master’s degree or advanced specialization (experience)
- Interest in earning advanced cloud or ML certifications (AWS/GCP/Azure), or possession of relevant certifications already (experience)
- Agile mindset, adaptability, initiative, and strong critical thinking (experience)
- In-depth understanding of data governance principles and data lifecycle management (experience)
- Significant experience with advanced data visualization and data storytelling, supporting executive-level decision-making (experience)
- Familiarity with cloud-based ML pipelines, distributed training, and scalable deployment technologies (experience)
- Advanced knowledge of statistical analysis, complex ML algorithms, and large-scale data processing techniques (experience)
Responsibilities
- Lead cross-functional collaboration with business stakeholders, data engineers, and technical teams, driving the definition of data-driven problems and opportunities
- Provide technical leadership, promoting quality standards, code reviews, experimentation, and best practices across the team
- Oversee complex data quality investigations and model performance issues, ensuring scalability, robustness, and long-term maintainability
- Design, develop, and validate advanced machine learning solutions, including supervised, unsupervised, and deep learning approaches that deliver measurable business impact
- Coordinate and optimize data preprocessing, feature engineering, and in-depth exploratory data analysis, establishing efficient and reproducible workflows
- Produce and review high-quality technical documentation, ensuring alignment with data architectures, internal standards, and compliance requirements
Benefits
- general: Opportunity to learn and develop yourself and your career
- general: Encouragement to be courageous and experiment every day
- general: Continuous trust and support in an environment where everyone can thrive
- general: Flexible working patterns
- general: Equal-opportunity employment
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Senior Data Scientist - Artificial Intelligence
IBM
Senior Data Scientist - Artificial Intelligence
Job Description
Senior Data Scientist - Artificial Intelligence
📋 Job Overview
As a Senior Data Scientist at IBM Consulting, you will lead complex AI initiatives across various industries, providing technical leadership and shaping client AI strategies. You will drive innovation, collaborate with diverse teams, and develop advanced machine learning solutions to deliver measurable business impact.
📍 Location: Multiple Locations (Remote/Hybrid)
💼 Career Level: Professional
🎯 Key Responsibilities
- Lead cross-functional collaboration with business stakeholders, data engineers, and technical teams, driving the definition of data-driven problems and opportunities
- Provide technical leadership, promoting quality standards, code reviews, experimentation, and best practices across the team
- Oversee complex data quality investigations and model performance issues, ensuring scalability, robustness, and long-term maintainability
- Design, develop, and validate advanced machine learning solutions, including supervised, unsupervised, and deep learning approaches that deliver measurable business impact
- Coordinate and optimize data preprocessing, feature engineering, and in-depth exploratory data analysis, establishing efficient and reproducible workflows
- Produce and review high-quality technical documentation, ensuring alignment with data architectures, internal standards, and compliance requirements
✅ Required Qualifications
- Minimum 6 years of professional experience as a Data Scientist, with demonstrated ownership of end-to-end AI/ML projects in enterprise environments
- Academic background in data science, statistics, computer science, or related disciplines
- Strong command of machine learning, advanced statistical methods, model evaluation, and optimization techniques
- Proven ability to lead technical teams and projects, identifying bottlenecks, defining priorities, and ensuring delivery excellence
- Strong understanding of data modeling concepts and relational/NoSQL database principles
- Advanced proficiency in Python and SQL, including ML frameworks such as scikit-learn, PyTorch, or TensorFlow
- Hands-on experience deploying models into production, including MLOps practices (CI/CD, monitoring, drift management, reproducibility)
⭐ Preferred Qualifications
- Master’s degree or advanced specialization
- Interest in earning advanced cloud or ML certifications (AWS/GCP/Azure), or possession of relevant certifications already
- Agile mindset, adaptability, initiative, and strong critical thinking
- In-depth understanding of data governance principles and data lifecycle management
- Significant experience with advanced data visualization and data storytelling, supporting executive-level decision-making
- Familiarity with cloud-based ML pipelines, distributed training, and scalable deployment technologies
- Advanced knowledge of statistical analysis, complex ML algorithms, and large-scale data processing techniques
🛠️ Required Skills
- Machine learning
- Advanced statistical methods
- Model evaluation
- Optimization techniques
- Data modeling
- Relational databases
- NoSQL databases
- Python
- SQL
- scikit-learn
- PyTorch
- TensorFlow
- MLOps
- CI/CD
- Monitoring
- Drift management
- Reproducibility
- Data governance
- Data lifecycle management
- Data visualization
- Data storytelling
- Cloud-based ML pipelines
- Distributed training
- Scalable deployment
- Statistical analysis
- Complex ML algorithms
- Large-scale data processing
- Agile mindset
- Adaptability
- Initiative
- Critical thinking
- Leadership
- Collaboration
🎁 Benefits & Perks
- Opportunity to learn and develop yourself and your career
- Encouragement to be courageous and experiment every day
- Continuous trust and support in an environment where everyone can thrive
- Flexible working patterns
- Equal-opportunity employment
Locations
- Multiple Locations, India (Remote)
Salary
Estimated Salary Rangemedium confidence
2,500,000 - 4,200,000 INR / yearly
Source: ai estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Machine learningintermediate
- Advanced statistical methodsintermediate
- Model evaluationintermediate
- Optimization techniquesintermediate
- Data modelingintermediate
- Relational databasesintermediate
- NoSQL databasesintermediate
- Pythonintermediate
- SQLintermediate
- scikit-learnintermediate
- PyTorchintermediate
- TensorFlowintermediate
- MLOpsintermediate
- CI/CDintermediate
- Monitoringintermediate
- Drift managementintermediate
- Reproducibilityintermediate
- Data governanceintermediate
- Data lifecycle managementintermediate
- Data visualizationintermediate
- Data storytellingintermediate
- Cloud-based ML pipelinesintermediate
- Distributed trainingintermediate
- Scalable deploymentintermediate
- Statistical analysisintermediate
- Complex ML algorithmsintermediate
- Large-scale data processingintermediate
- Agile mindsetintermediate
- Adaptabilityintermediate
- Initiativeintermediate
- Critical thinkingintermediate
- Leadershipintermediate
- Collaborationintermediate
Required Qualifications
- Minimum 6 years of professional experience as a Data Scientist, with demonstrated ownership of end-to-end AI/ML projects in enterprise environments (experience)
- Academic background in data science, statistics, computer science, or related disciplines (experience)
- Strong command of machine learning, advanced statistical methods, model evaluation, and optimization techniques (experience)
- Proven ability to lead technical teams and projects, identifying bottlenecks, defining priorities, and ensuring delivery excellence (experience)
- Strong understanding of data modeling concepts and relational/NoSQL database principles (experience)
- Advanced proficiency in Python and SQL, including ML frameworks such as scikit-learn, PyTorch, or TensorFlow (experience)
- Hands-on experience deploying models into production, including MLOps practices (CI/CD, monitoring, drift management, reproducibility) (experience)
Preferred Qualifications
- Master’s degree or advanced specialization (experience)
- Interest in earning advanced cloud or ML certifications (AWS/GCP/Azure), or possession of relevant certifications already (experience)
- Agile mindset, adaptability, initiative, and strong critical thinking (experience)
- In-depth understanding of data governance principles and data lifecycle management (experience)
- Significant experience with advanced data visualization and data storytelling, supporting executive-level decision-making (experience)
- Familiarity with cloud-based ML pipelines, distributed training, and scalable deployment technologies (experience)
- Advanced knowledge of statistical analysis, complex ML algorithms, and large-scale data processing techniques (experience)
Responsibilities
- Lead cross-functional collaboration with business stakeholders, data engineers, and technical teams, driving the definition of data-driven problems and opportunities
- Provide technical leadership, promoting quality standards, code reviews, experimentation, and best practices across the team
- Oversee complex data quality investigations and model performance issues, ensuring scalability, robustness, and long-term maintainability
- Design, develop, and validate advanced machine learning solutions, including supervised, unsupervised, and deep learning approaches that deliver measurable business impact
- Coordinate and optimize data preprocessing, feature engineering, and in-depth exploratory data analysis, establishing efficient and reproducible workflows
- Produce and review high-quality technical documentation, ensuring alignment with data architectures, internal standards, and compliance requirements
Benefits
- general: Opportunity to learn and develop yourself and your career
- general: Encouragement to be courageous and experiment every day
- general: Continuous trust and support in an environment where everyone can thrive
- general: Flexible working patterns
- general: Equal-opportunity employment
Tags & Categories
Answer 10 quick questions to check your fit for Senior Data Scientist - Artificial Intelligence @ IBM.
10 Questions
~2 Minutes
Instant Score
Related Books and Jobs