Senior Associate IS Bus Sys Analyst-Automation and Analytics
Amgen
Senior Associate IS Bus Sys Analyst-Automation and Analytics
Job Description
ABOUT AMGEN
What you will do
- Design, develop, and maintain predictive models, decision support tools, and dashboards using Python, R, SQL, Power BI, or similar platforms.
- Partner with delivery teams to embed data science outputs into business operations, focusing on improving efficiency, reliability, and end-user experience in Digital Workplace services.
- Build and automate data pipelines for data ingestion, cleansing, transformation, and model training using structured and unstructured datasets.
- Monitor, maintain, and tune models to ensure accuracy, interpretability, and sustained business impact.
- Support efforts to operationalize ML models by working with data engineers and platform teams on integration and automation.
- Conduct data exploration, hypothesis testing, and statistical analysis to identify optimization opportunities across services like endpoint health, service desk operations, mobile technology, and collaboration platforms.
- Provide ad hoc and recurring data-driven recommendations to improve automation performance, service delivery, and capacity forecasting.
- Develop reusable components, templates, and frameworks that support analytics and automation scalability across DWX.
- Collaborate with other data scientists, analysts, and developers to implement best practices in model development and lifecycle management.
What we expect of you
- Master's degree / Bachelor's degree and 5 to 9 years in Data Science, Computer Science, IT, or related field
Must-Have Skills
- Data Science & ML: Proficient in Python (preferred) or R; experience with libraries such as scikit-learn, pandas, NumPy, XGBoost, and familiarity with TensorFlow or PyTorch. Skilled in supervised/unsupervised learning, time series forecasting, feature engineering, and hyper parameter tuning.
- SQL & Data Engineering: Advanced SQL (joins, CTEs, window functions, optimization) with experience in relational databases (PostgreSQL, SQL Server, Oracle, MySQL) and data modeling (star/snowflake schema).
- Databricks: Hands-on experience with Databricks notebooks (Python, SQL, PySpark), Delta Lake, MLflow, Unity Catalog, and Lakehouse architecture.
- Visualization: Power BI expertise (DAX, Power Query, data model optimization) and/or Tableau (advanced calculations, LODs, performance tuning).
- MLOps & Automation: Experience with Airflow, SageMaker, or Azure ML; knowledge of CI/CD for ML models and automated data pipelines.
- Generative AI: Understanding of foundation models (LLMs, transformers, diffusion); hands-on experience with APIs from OpenAI, Azure OpenAI, HuggingFace; prompt engineering for summarization, classification, Q&A, and content generation; bonus for fine-tuning, RAG pipelines, or vector databases (FAISS, Pinecone).
- Cloud Platforms: Familiarity with Azure (Data Factory, Synapse, Azure ML, Azure OpenAI), AWS (S3, Redshift, SageMaker), or GCP (BigQuery, Vertex AI).
- ITSM & Agile: Exposure to ITIL practices or ITSM platforms (e.g., ServiceNow) and working knowledge of Agile/SAFe environments.
- Analytical mindset with attention to detail and data integrity.
- Strong problem-solving and critical thinking skills.
- Ability to work independently and drive tasks to completion.
- Strong collaboration and teamwork skills.
- Adaptability in a fast-paced, evolving environment.
- Clear and concise documentation habits.
Locations
- Hyderabad, India
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
25,000 - 35,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Data Science & ML: Proficient in Python (preferred) or R; experience with libraries such as scikit-learn, pandas, NumPy, XGBoost, and familiarity with TensorFlow or PyTorch. Skilled in supervised/unsupervised learning, time series forecasting, feature engineering, and hyper parameter tuning.intermediate
- SQL & Data Engineering: Advanced SQL (joins, CTEs, window functions, optimization) with experience in relational databases (PostgreSQL, SQL Server, Oracle, MySQL) and data modeling (star/snowflake schema).intermediate
- Databricks: Hands-on experience with Databricks notebooks (Python, SQL, PySpark), Delta Lake, MLflow, Unity Catalog, and Lakehouse architecture.intermediate
- Visualization: Power BI expertise (DAX, Power Query, data model optimization) and/or Tableau (advanced calculations, LODs, performance tuning).intermediate
- MLOps & Automation: Experience with Airflow, SageMaker, or Azure ML; knowledge of CI/CD for ML models and automated data pipelines.intermediate
- Generative AI: Understanding of foundation models (LLMs, transformers, diffusion); hands-on experience with APIs from OpenAI, Azure OpenAI, HuggingFace; prompt engineering for summarization, classification, Q&A, and content generation; bonus for fine-tuning, RAG pipelines, or vector databases (FAISS, Pinecone).intermediate
- Cloud Platforms: Familiarity with Azure (Data Factory, Synapse, Azure ML, Azure OpenAI), AWS (S3, Redshift, SageMaker), or GCP (BigQuery, Vertex AI).intermediate
- ITSM & Agile: Exposure to ITIL practices or ITSM platforms (e.g., ServiceNow) and working knowledge of Agile/SAFe environments.intermediate
- Analytical mindset with attention to detail and data integrity.intermediate
- Strong problem-solving and critical thinking skills.intermediate
- Ability to work independently and drive tasks to completion.intermediate
- Strong collaboration and teamwork skills.intermediate
- Adaptability in a fast-paced, evolving environment.intermediate
- Clear and concise documentation habits.intermediate
Required Qualifications
- Master's degree / Bachelor's degree and 5 to 9 years in Data Science, Computer Science, IT, or related field (experience)
Responsibilities
- Design, develop, and maintain predictive models, decision support tools, and dashboards using Python, R, SQL, Power BI, or similar platforms.
- Partner with delivery teams to embed data science outputs into business operations, focusing on improving efficiency, reliability, and end-user experience in Digital Workplace services.
- Build and automate data pipelines for data ingestion, cleansing, transformation, and model training using structured and unstructured datasets.
- Monitor, maintain, and tune models to ensure accuracy, interpretability, and sustained business impact.
- Support efforts to operationalize ML models by working with data engineers and platform teams on integration and automation.
- Conduct data exploration, hypothesis testing, and statistical analysis to identify optimization opportunities across services like endpoint health, service desk operations, mobile technology, and collaboration platforms.
- Provide ad hoc and recurring data-driven recommendations to improve automation performance, service delivery, and capacity forecasting.
- Develop reusable components, templates, and frameworks that support analytics and automation scalability across DWX.
- Collaborate with other data scientists, analysts, and developers to implement best practices in model development and lifecycle management.
Tags & Categories
Senior Associate IS Bus Sys Analyst-Automation and Analytics
Amgen
Senior Associate IS Bus Sys Analyst-Automation and Analytics
Job Description
ABOUT AMGEN
What you will do
- Design, develop, and maintain predictive models, decision support tools, and dashboards using Python, R, SQL, Power BI, or similar platforms.
- Partner with delivery teams to embed data science outputs into business operations, focusing on improving efficiency, reliability, and end-user experience in Digital Workplace services.
- Build and automate data pipelines for data ingestion, cleansing, transformation, and model training using structured and unstructured datasets.
- Monitor, maintain, and tune models to ensure accuracy, interpretability, and sustained business impact.
- Support efforts to operationalize ML models by working with data engineers and platform teams on integration and automation.
- Conduct data exploration, hypothesis testing, and statistical analysis to identify optimization opportunities across services like endpoint health, service desk operations, mobile technology, and collaboration platforms.
- Provide ad hoc and recurring data-driven recommendations to improve automation performance, service delivery, and capacity forecasting.
- Develop reusable components, templates, and frameworks that support analytics and automation scalability across DWX.
- Collaborate with other data scientists, analysts, and developers to implement best practices in model development and lifecycle management.
What we expect of you
- Master's degree / Bachelor's degree and 5 to 9 years in Data Science, Computer Science, IT, or related field
Must-Have Skills
- Data Science & ML: Proficient in Python (preferred) or R; experience with libraries such as scikit-learn, pandas, NumPy, XGBoost, and familiarity with TensorFlow or PyTorch. Skilled in supervised/unsupervised learning, time series forecasting, feature engineering, and hyper parameter tuning.
- SQL & Data Engineering: Advanced SQL (joins, CTEs, window functions, optimization) with experience in relational databases (PostgreSQL, SQL Server, Oracle, MySQL) and data modeling (star/snowflake schema).
- Databricks: Hands-on experience with Databricks notebooks (Python, SQL, PySpark), Delta Lake, MLflow, Unity Catalog, and Lakehouse architecture.
- Visualization: Power BI expertise (DAX, Power Query, data model optimization) and/or Tableau (advanced calculations, LODs, performance tuning).
- MLOps & Automation: Experience with Airflow, SageMaker, or Azure ML; knowledge of CI/CD for ML models and automated data pipelines.
- Generative AI: Understanding of foundation models (LLMs, transformers, diffusion); hands-on experience with APIs from OpenAI, Azure OpenAI, HuggingFace; prompt engineering for summarization, classification, Q&A, and content generation; bonus for fine-tuning, RAG pipelines, or vector databases (FAISS, Pinecone).
- Cloud Platforms: Familiarity with Azure (Data Factory, Synapse, Azure ML, Azure OpenAI), AWS (S3, Redshift, SageMaker), or GCP (BigQuery, Vertex AI).
- ITSM & Agile: Exposure to ITIL practices or ITSM platforms (e.g., ServiceNow) and working knowledge of Agile/SAFe environments.
- Analytical mindset with attention to detail and data integrity.
- Strong problem-solving and critical thinking skills.
- Ability to work independently and drive tasks to completion.
- Strong collaboration and teamwork skills.
- Adaptability in a fast-paced, evolving environment.
- Clear and concise documentation habits.
Locations
- Hyderabad, India
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
25,000 - 35,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Data Science & ML: Proficient in Python (preferred) or R; experience with libraries such as scikit-learn, pandas, NumPy, XGBoost, and familiarity with TensorFlow or PyTorch. Skilled in supervised/unsupervised learning, time series forecasting, feature engineering, and hyper parameter tuning.intermediate
- SQL & Data Engineering: Advanced SQL (joins, CTEs, window functions, optimization) with experience in relational databases (PostgreSQL, SQL Server, Oracle, MySQL) and data modeling (star/snowflake schema).intermediate
- Databricks: Hands-on experience with Databricks notebooks (Python, SQL, PySpark), Delta Lake, MLflow, Unity Catalog, and Lakehouse architecture.intermediate
- Visualization: Power BI expertise (DAX, Power Query, data model optimization) and/or Tableau (advanced calculations, LODs, performance tuning).intermediate
- MLOps & Automation: Experience with Airflow, SageMaker, or Azure ML; knowledge of CI/CD for ML models and automated data pipelines.intermediate
- Generative AI: Understanding of foundation models (LLMs, transformers, diffusion); hands-on experience with APIs from OpenAI, Azure OpenAI, HuggingFace; prompt engineering for summarization, classification, Q&A, and content generation; bonus for fine-tuning, RAG pipelines, or vector databases (FAISS, Pinecone).intermediate
- Cloud Platforms: Familiarity with Azure (Data Factory, Synapse, Azure ML, Azure OpenAI), AWS (S3, Redshift, SageMaker), or GCP (BigQuery, Vertex AI).intermediate
- ITSM & Agile: Exposure to ITIL practices or ITSM platforms (e.g., ServiceNow) and working knowledge of Agile/SAFe environments.intermediate
- Analytical mindset with attention to detail and data integrity.intermediate
- Strong problem-solving and critical thinking skills.intermediate
- Ability to work independently and drive tasks to completion.intermediate
- Strong collaboration and teamwork skills.intermediate
- Adaptability in a fast-paced, evolving environment.intermediate
- Clear and concise documentation habits.intermediate
Required Qualifications
- Master's degree / Bachelor's degree and 5 to 9 years in Data Science, Computer Science, IT, or related field (experience)
Responsibilities
- Design, develop, and maintain predictive models, decision support tools, and dashboards using Python, R, SQL, Power BI, or similar platforms.
- Partner with delivery teams to embed data science outputs into business operations, focusing on improving efficiency, reliability, and end-user experience in Digital Workplace services.
- Build and automate data pipelines for data ingestion, cleansing, transformation, and model training using structured and unstructured datasets.
- Monitor, maintain, and tune models to ensure accuracy, interpretability, and sustained business impact.
- Support efforts to operationalize ML models by working with data engineers and platform teams on integration and automation.
- Conduct data exploration, hypothesis testing, and statistical analysis to identify optimization opportunities across services like endpoint health, service desk operations, mobile technology, and collaboration platforms.
- Provide ad hoc and recurring data-driven recommendations to improve automation performance, service delivery, and capacity forecasting.
- Develop reusable components, templates, and frameworks that support analytics and automation scalability across DWX.
- Collaborate with other data scientists, analysts, and developers to implement best practices in model development and lifecycle management.
Tags & Categories