Senior Machine Learning Engineer
Amgen
Senior Machine Learning Engineer
Job Description
Join Amgen’s Mission of Serving Patients
What you will do
- Develop data flow pipelines to extract, transform, and load data from various data sources in various data format to enterprise data lake and data warehouse system in three regions in AWS
- Provide data analytics and predictive analysis to business users
- Assist in design and development of the data pipeline for Global Data and Analytics team, such as data cleaning and transformation
- Explore, understand various datasets used in biotech/pharma commercial data analytics
- Create informative and appealing data visualizations
- Work with Data Scientist to perform data cleaning, statistical analysis, feature engineering
- Develop pipeline for model selection, training, and evaluation
- Understand experimental design and conducting A/B tests for data-driven decision-making
- Ensure consistent feature engineering between training and model serving
- Automate model deployment, monitoring, model retrain process
- Adhere to best practices for coding, testing and designing reusable code/component
- Flexible to work on data engineering or machine learning projects based on current product backlog within the team
- Explore new tools, technologies that will help to improve ETL platform performance and machine learning operations
- Work effectively in cross-functional teams and collaborate with data engineers, analysts, and business stakeholders
- Convey insights and findings to non-technical stakeholders
- Stay updated with the latest trends and advancements in data science/machine learning technologies
- Mentor junior data/machine learning engineer
What we expect of you
- Doctorate degree OR Master’s degree and 2 years of Data Science/Machine Learning experience OR Bachelor’s degree and 4 years of Data Science/Machine Learning experience OR Associate’s degree and 8 years of Data Science/Machine Learning experience OR High school diploma / GED and 10 years of Data Science/Machine Learning experience
- Strong programming skills in Python or R, library and packages related to data manipulation, statistical analysis, chart/plot, and machine learning algorithms and framework
- Outstanding analytical and problem-solving skills; Ability to learn quickly; experience in model selection, training, and evaluation
- Familiar with PySpark dataframe and data processing libraries, machine learning frameworks (like Tensorflow, Keras or PyTorch), and other machine learning libraries
- Familiar with Machine Learning life cycle, be able to implement feature store, MLflow, model registry, model deployment, model serving, model monitoring
- Proficiency in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification
- Experience with data modeling for both OLAP and OLTP databases, hands-on experience with SQL, especially SparkSQL performance tuning
- Experience with software DevOps CI/CD tools, GitLab
- Familiar with AWS, Azure, or Google Cloud
- Knowledge of NLP techniques for text analysis and sentiment analysis
- Experience in analyzing time-series data for forecasting and trend analysis
- Experience with docker container, Kubernetes container orchestration
- Experience with Databricks, Apache Airflow and Apache Spark; Spark performance turning
- Experience with Pharmaceutical industry, commercial operations
Must-Have Skills
- Python or R programming
- Data manipulation, statistical analysis, chart/plot
- Machine learning algorithms and framework
- PySpark dataframe and data processing libraries
- Tensorflow, Keras or PyTorch
- Machine Learning life cycle
- Feature store, MLflow, model registry, model deployment, model serving, model monitoring
- Statistical techniques and hypothesis testing
- Regression analysis, clustering and classification
- Data modeling for OLAP and OLTP databases
- SQL, SparkSQL performance tuning
- Software DevOps CI/CD tools, GitLab
- AWS, Azure, or Google Cloud
- NLP techniques for text analysis and sentiment analysis
- Time-series data analysis for forecasting and trend analysis
- Docker container, Kubernetes container orchestration
- Databricks, Apache Airflow, Apache Spark
- Spark performance tuning
- Pharmaceutical industry, commercial operations
What you can expect of us
- Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
- Group medical, dental and vision coverage
- Life and disability insurance
- Flexible spending accounts
- Discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
- Stock-based long-term incentives
- Award-winning time-off plans
- Flexible work models, including remote and hybrid work arrangements, where possible
Locations
- Thousand Oaks, United States of America (Remote)
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
150,000 - 200,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Python or R programmingintermediate
- Data manipulation, statistical analysis, chart/plotintermediate
- Machine learning algorithms and frameworkintermediate
- PySpark dataframe and data processing librariesintermediate
- Tensorflow, Keras or PyTorchintermediate
- Machine Learning life cycleintermediate
- Feature store, MLflow, model registry, model deployment, model serving, model monitoringintermediate
- Statistical techniques and hypothesis testingintermediate
- Regression analysis, clustering and classificationintermediate
- Data modeling for OLAP and OLTP databasesintermediate
- SQL, SparkSQL performance tuningintermediate
- Software DevOps CI/CD tools, GitLabintermediate
- AWS, Azure, or Google Cloudintermediate
- NLP techniques for text analysis and sentiment analysisintermediate
- Time-series data analysis for forecasting and trend analysisintermediate
- Docker container, Kubernetes container orchestrationintermediate
- Databricks, Apache Airflow, Apache Sparkintermediate
- Spark performance tuningintermediate
- Pharmaceutical industry, commercial operationsintermediate
Required Qualifications
- Doctorate degree OR Master’s degree and 2 years of Data Science/Machine Learning experience OR Bachelor’s degree and 4 years of Data Science/Machine Learning experience OR Associate’s degree and 8 years of Data Science/Machine Learning experience OR High school diploma / GED and 10 years of Data Science/Machine Learning experience (experience)
- Strong programming skills in Python or R, library and packages related to data manipulation, statistical analysis, chart/plot, and machine learning algorithms and framework (experience)
- Outstanding analytical and problem-solving skills; Ability to learn quickly; experience in model selection, training, and evaluation (experience)
- Familiar with PySpark dataframe and data processing libraries, machine learning frameworks (like Tensorflow, Keras or PyTorch), and other machine learning libraries (experience)
- Familiar with Machine Learning life cycle, be able to implement feature store, MLflow, model registry, model deployment, model serving, model monitoring (experience)
- Proficiency in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification (experience)
- Experience with data modeling for both OLAP and OLTP databases, hands-on experience with SQL, especially SparkSQL performance tuning (experience)
- Experience with software DevOps CI/CD tools, GitLab (experience)
- Familiar with AWS, Azure, or Google Cloud (experience)
- Knowledge of NLP techniques for text analysis and sentiment analysis (experience)
- Experience in analyzing time-series data for forecasting and trend analysis (experience)
- Experience with docker container, Kubernetes container orchestration (experience)
- Experience with Databricks, Apache Airflow and Apache Spark; Spark performance turning (experience)
- Experience with Pharmaceutical industry, commercial operations (experience)
Responsibilities
- Develop data flow pipelines to extract, transform, and load data from various data sources in various data format to enterprise data lake and data warehouse system in three regions in AWS
- Provide data analytics and predictive analysis to business users
- Assist in design and development of the data pipeline for Global Data and Analytics team, such as data cleaning and transformation
- Explore, understand various datasets used in biotech/pharma commercial data analytics
- Create informative and appealing data visualizations
- Work with Data Scientist to perform data cleaning, statistical analysis, feature engineering
- Develop pipeline for model selection, training, and evaluation
- Understand experimental design and conducting A/B tests for data-driven decision-making
- Ensure consistent feature engineering between training and model serving
- Automate model deployment, monitoring, model retrain process
- Adhere to best practices for coding, testing and designing reusable code/component
- Flexible to work on data engineering or machine learning projects based on current product backlog within the team
- Explore new tools, technologies that will help to improve ETL platform performance and machine learning operations
- Work effectively in cross-functional teams and collaborate with data engineers, analysts, and business stakeholders
- Convey insights and findings to non-technical stakeholders
- Stay updated with the latest trends and advancements in data science/machine learning technologies
- Mentor junior data/machine learning engineer
Benefits
- general: Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
- general: Group medical, dental and vision coverage
- general: Life and disability insurance
- general: Flexible spending accounts
- general: Discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
- general: Stock-based long-term incentives
- general: Award-winning time-off plans
- general: Flexible work models, including remote and hybrid work arrangements, where possible
Tags & Categories
Senior Machine Learning Engineer
Amgen
Senior Machine Learning Engineer
Job Description
Join Amgen’s Mission of Serving Patients
What you will do
- Develop data flow pipelines to extract, transform, and load data from various data sources in various data format to enterprise data lake and data warehouse system in three regions in AWS
- Provide data analytics and predictive analysis to business users
- Assist in design and development of the data pipeline for Global Data and Analytics team, such as data cleaning and transformation
- Explore, understand various datasets used in biotech/pharma commercial data analytics
- Create informative and appealing data visualizations
- Work with Data Scientist to perform data cleaning, statistical analysis, feature engineering
- Develop pipeline for model selection, training, and evaluation
- Understand experimental design and conducting A/B tests for data-driven decision-making
- Ensure consistent feature engineering between training and model serving
- Automate model deployment, monitoring, model retrain process
- Adhere to best practices for coding, testing and designing reusable code/component
- Flexible to work on data engineering or machine learning projects based on current product backlog within the team
- Explore new tools, technologies that will help to improve ETL platform performance and machine learning operations
- Work effectively in cross-functional teams and collaborate with data engineers, analysts, and business stakeholders
- Convey insights and findings to non-technical stakeholders
- Stay updated with the latest trends and advancements in data science/machine learning technologies
- Mentor junior data/machine learning engineer
What we expect of you
- Doctorate degree OR Master’s degree and 2 years of Data Science/Machine Learning experience OR Bachelor’s degree and 4 years of Data Science/Machine Learning experience OR Associate’s degree and 8 years of Data Science/Machine Learning experience OR High school diploma / GED and 10 years of Data Science/Machine Learning experience
- Strong programming skills in Python or R, library and packages related to data manipulation, statistical analysis, chart/plot, and machine learning algorithms and framework
- Outstanding analytical and problem-solving skills; Ability to learn quickly; experience in model selection, training, and evaluation
- Familiar with PySpark dataframe and data processing libraries, machine learning frameworks (like Tensorflow, Keras or PyTorch), and other machine learning libraries
- Familiar with Machine Learning life cycle, be able to implement feature store, MLflow, model registry, model deployment, model serving, model monitoring
- Proficiency in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification
- Experience with data modeling for both OLAP and OLTP databases, hands-on experience with SQL, especially SparkSQL performance tuning
- Experience with software DevOps CI/CD tools, GitLab
- Familiar with AWS, Azure, or Google Cloud
- Knowledge of NLP techniques for text analysis and sentiment analysis
- Experience in analyzing time-series data for forecasting and trend analysis
- Experience with docker container, Kubernetes container orchestration
- Experience with Databricks, Apache Airflow and Apache Spark; Spark performance turning
- Experience with Pharmaceutical industry, commercial operations
Must-Have Skills
- Python or R programming
- Data manipulation, statistical analysis, chart/plot
- Machine learning algorithms and framework
- PySpark dataframe and data processing libraries
- Tensorflow, Keras or PyTorch
- Machine Learning life cycle
- Feature store, MLflow, model registry, model deployment, model serving, model monitoring
- Statistical techniques and hypothesis testing
- Regression analysis, clustering and classification
- Data modeling for OLAP and OLTP databases
- SQL, SparkSQL performance tuning
- Software DevOps CI/CD tools, GitLab
- AWS, Azure, or Google Cloud
- NLP techniques for text analysis and sentiment analysis
- Time-series data analysis for forecasting and trend analysis
- Docker container, Kubernetes container orchestration
- Databricks, Apache Airflow, Apache Spark
- Spark performance tuning
- Pharmaceutical industry, commercial operations
What you can expect of us
- Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
- Group medical, dental and vision coverage
- Life and disability insurance
- Flexible spending accounts
- Discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
- Stock-based long-term incentives
- Award-winning time-off plans
- Flexible work models, including remote and hybrid work arrangements, where possible
Locations
- Thousand Oaks, United States of America (Remote)
Salary
Salary not disclosed
Estimated Salary Rangehigh confidence
150,000 - 200,000 USD / yearly
Source: xAI estimated
* This is an estimated range based on market data and may vary based on experience and qualifications.
Skills Required
- Python or R programmingintermediate
- Data manipulation, statistical analysis, chart/plotintermediate
- Machine learning algorithms and frameworkintermediate
- PySpark dataframe and data processing librariesintermediate
- Tensorflow, Keras or PyTorchintermediate
- Machine Learning life cycleintermediate
- Feature store, MLflow, model registry, model deployment, model serving, model monitoringintermediate
- Statistical techniques and hypothesis testingintermediate
- Regression analysis, clustering and classificationintermediate
- Data modeling for OLAP and OLTP databasesintermediate
- SQL, SparkSQL performance tuningintermediate
- Software DevOps CI/CD tools, GitLabintermediate
- AWS, Azure, or Google Cloudintermediate
- NLP techniques for text analysis and sentiment analysisintermediate
- Time-series data analysis for forecasting and trend analysisintermediate
- Docker container, Kubernetes container orchestrationintermediate
- Databricks, Apache Airflow, Apache Sparkintermediate
- Spark performance tuningintermediate
- Pharmaceutical industry, commercial operationsintermediate
Required Qualifications
- Doctorate degree OR Master’s degree and 2 years of Data Science/Machine Learning experience OR Bachelor’s degree and 4 years of Data Science/Machine Learning experience OR Associate’s degree and 8 years of Data Science/Machine Learning experience OR High school diploma / GED and 10 years of Data Science/Machine Learning experience (experience)
- Strong programming skills in Python or R, library and packages related to data manipulation, statistical analysis, chart/plot, and machine learning algorithms and framework (experience)
- Outstanding analytical and problem-solving skills; Ability to learn quickly; experience in model selection, training, and evaluation (experience)
- Familiar with PySpark dataframe and data processing libraries, machine learning frameworks (like Tensorflow, Keras or PyTorch), and other machine learning libraries (experience)
- Familiar with Machine Learning life cycle, be able to implement feature store, MLflow, model registry, model deployment, model serving, model monitoring (experience)
- Proficiency in statistical techniques and hypothesis testing, experience with regression analysis, clustering and classification (experience)
- Experience with data modeling for both OLAP and OLTP databases, hands-on experience with SQL, especially SparkSQL performance tuning (experience)
- Experience with software DevOps CI/CD tools, GitLab (experience)
- Familiar with AWS, Azure, or Google Cloud (experience)
- Knowledge of NLP techniques for text analysis and sentiment analysis (experience)
- Experience in analyzing time-series data for forecasting and trend analysis (experience)
- Experience with docker container, Kubernetes container orchestration (experience)
- Experience with Databricks, Apache Airflow and Apache Spark; Spark performance turning (experience)
- Experience with Pharmaceutical industry, commercial operations (experience)
Responsibilities
- Develop data flow pipelines to extract, transform, and load data from various data sources in various data format to enterprise data lake and data warehouse system in three regions in AWS
- Provide data analytics and predictive analysis to business users
- Assist in design and development of the data pipeline for Global Data and Analytics team, such as data cleaning and transformation
- Explore, understand various datasets used in biotech/pharma commercial data analytics
- Create informative and appealing data visualizations
- Work with Data Scientist to perform data cleaning, statistical analysis, feature engineering
- Develop pipeline for model selection, training, and evaluation
- Understand experimental design and conducting A/B tests for data-driven decision-making
- Ensure consistent feature engineering between training and model serving
- Automate model deployment, monitoring, model retrain process
- Adhere to best practices for coding, testing and designing reusable code/component
- Flexible to work on data engineering or machine learning projects based on current product backlog within the team
- Explore new tools, technologies that will help to improve ETL platform performance and machine learning operations
- Work effectively in cross-functional teams and collaborate with data engineers, analysts, and business stakeholders
- Convey insights and findings to non-technical stakeholders
- Stay updated with the latest trends and advancements in data science/machine learning technologies
- Mentor junior data/machine learning engineer
Benefits
- general: Comprehensive employee benefits package, including a Retirement and Savings Plan with generous company contributions
- general: Group medical, dental and vision coverage
- general: Life and disability insurance
- general: Flexible spending accounts
- general: Discretionary annual bonus program, or for field sales representatives, a sales-based incentive plan
- general: Stock-based long-term incentives
- general: Award-winning time-off plans
- general: Flexible work models, including remote and hybrid work arrangements, where possible
Tags & Categories