A
Data & AI Engineer
AMARIS GROUP SA
2 hours ago
Full-time
On-site
Cambridge, Massachusetts, United States
We are looking for a
Data & AI Engineer
to join our team and
contribute to data engineering, analysis, and AI/ML initiatives within a healthcare technology environment . The ideal candidate has strong experience working with large and complex datasets, data quality and transformation, Python-based data processing, and cloud data platforms.
You will work closely with data, biomedical, IT, and engineering teams to ensure data is reliable, scalable, and ready to support advanced analytics, predictive algorithms, and AI-driven solutions. The focus of this role is data engineering and analysis.
We offer theopportunity to work on data and AI initiatives that make a meaningful impact in healthcare technology.Also, a collaborative environment spanning data, biomedical, IT, and engineering teams.
Location:
Cambridge (Massachusetts, US) Work Mode:
on-site Type of contract:
C2C
Key Responsibilities: Retrieve data from AWS S3 and query and analyze datasets using Amazon Athena, SageMaker, and Jupyter Notebooks. Clean, transform, and prepare large datasets from multiple sources. Identify missing data, inconsistencies, time-alignment discrepancies, and artifact-prone measurements.
Data Management Develop and maintain data processing pipelines that transform and parse CSV, XML, and other flat-file formats into scalable databases and data structures such as InfluxDB. Develop and run Python and Bash scripts to automate ingestion, inventory, validation, and quality assessment of incoming device data.
Data Integrity Evaluation Investigate data gaps, anomalies, and integrity issues using Athena, SageMaker, or Jupyter Notebooks. Determine whether root causes relate to data curation, technical configuration, system performance, or storage limitations.
Troubleshooting and Collaboration Collaborate with biomedical and IT teams at partner sites to troubleshoot data transfer and collection issues and ensure reliable data availability. Work closely with internal engineering, data, and technical teams to investigate and resolve data-related issues.
Data Modeling and AI/ML Analyze and model physiological, vital-sign, and EMR data from multiple sources to support data-driven and AI/ML solutions. Develop, enhance, and evaluate predictive algorithms and machine learning models using multivariate data, including numerical and textual data.
Statistical Validation Cross-validate Python-based machine learning and regression results against outputs generated in SAS, in collaboration with biostatistics teams.
Data Simulation Prepare and transform retrospective CSV/XML datasets to generate simulated data using a Virtual Hospital Simulator. Support algorithm testing and validation within surveillance software. Contribute to improving data quality, scalability, reliability, and analytical capabilities across data and AI initiatives.
Required Qualifications: 7 to 10 years of professional experience in data engineering and data analysis . Hands-on AI/ML experience , including developing, evaluating, and enhancing predictive algorithms and machine learning and regression models using multivariate data (numerical and text-based). Experience with healthcare data , including physiological signals, vital signs, and EMR data. Background in the medical device industry , including experience working with device-generated clinical data. Strong proficiency in
Python
for data processing, analysis, and machine learning. Experience scripting in Bash for batch processing and automation. Hands-on experience with
AWS data services , including S3, Athena, and SageMaker. Experience working in
Jupyter Notebooks
for exploratory analysis and data investigation. Proven experience parsing and transforming flat-file formats (CSV, XML) into scalable databases, with exposure to time-series databases such as InfluxDB. Demonstrated ability to identify and resolve data quality issues, including missing data, time-alignment problems, and measurement artifacts. Strong troubleshooting skills and the ability to diagnose root causes across data, configuration, and infrastructure. Excellent communication skills, with the ability to collaborate effectively with technical and non-technical stakeholders across multiple organizations.
Why choose us An international community bringing together more than 110 different nationalities An environment where trust is central: 70% of our leaders started their careers at the entry level A strong training system with our internal Academy and more than 250 modules available A dynamic work environment that frequently comes together for internal events (afterworks, team buildings, etc.)
Amaris Consulting promotes equal opportunities. We are committed to bringing together people from diverse backgrounds and creating an inclusive work environment. In this regard, we welcome applications from all qualified individuals, regardless of sex, sexual orientation, race, ethnicity, beliefs, age, marital status, disability, or other characteristics.
#LI-AS2
Job description We are looking for a
Data & AI Engineer
to join our team and
contribute to data engineering, analysis, and AI/ML initiatives within a healthcare technology environment . The ideal candidate has strong experience working with large and complex datasets, data quality and transformation, Python-based data processing, and cloud data platforms.
You will work closely with data, biomedical, IT, and engineering teams to ensure data is reliable, scalable, and ready to support advanced analytics, predictive algorithms, and AI-driven solutions. The focus of this role is data engineering and analysis.
We offer theopportunity to work on data and AI initiatives that make a meaningful impact in healthcare technology.Also, a collaborative environment spanning data, biomedical, IT, and engineering teams.
Location:
Cambridge (Massachusetts, US) Work Mode:
on-site Type of contract:
C2C
Key Responsibilities: Data Wrangling Retrieve data from AWS S3 and query and analyze datasets using Amazon Athena, SageMaker, and Jupyter Notebooks. Clean, transform, and prepare large datasets from multiple sources. Identify missing data, inconsistencies, time-alignment discrepancies, and artifact-prone measurements.
Data Management Develop and maintain data processing pipelines that transform and parse CSV, XML, and other flat-file formats into scalable databases and data structures such as InfluxDB. Develop and run Python and Bash scripts to automate ingestion, inventory, validation, and quality assessment of incoming device data.
Data Integrity Evaluation Investigate data gaps, anomalies, and integrity issues using Athena, SageMaker, or Jupyter Notebooks. Determine whether root causes relate to data curation, technical configuration, system performance, or storage limitations.
Troubleshooting and Collaboration Collaborate with biomedical and IT teams at partner sites to troubleshoot data transfer and collection issues and ensure reliable data availability. Work closely with internal engineering, data, and technical teams to investigate and resolve data-related issues.
Data Modeling and AI/ML Analyze and model physiological, vital-sign, and EMR data from multiple sources to support data-driven and AI/ML solutions. Develop, enhance, and evaluate predictive algorithms and machine learning models using multivariate data, including numerical and textual data.
Statistical Validation Cross-validate Python-based machine learning and regression results against outputs generated in SAS, in collaboration with biostatistics teams.
Data Simulation Prepare and transform retrospective CSV/XML datasets to generate simulated data using a Virtual Hospital Simulator. Support algorithm testing and validation within surveillance software.
Continuous Improvement Contribute to improving data quality, scalability, reliability, and analytical capabilities across data and AI initiatives.
Required Qualifications: 7 to 10 years of professional experience in data engineering and data analysis . Hands-on AI/ML experience , including developing, evaluating, and enhancing predictive algorithms and machine learning and regression models using multivariate data (numerical and text-based). Experience with healthcare data , including physiological signals, vital signs, and EMR data. Background in the medical device industry , including experience working with device-generated clinical data. Strong proficiency in
Python
for data processing, analysis, and machine learning. Experience scripting in Bash for batch processing and automation.
#J-18808-Ljbffr
Data & AI Engineer
to join our team and
contribute to data engineering, analysis, and AI/ML initiatives within a healthcare technology environment . The ideal candidate has strong experience working with large and complex datasets, data quality and transformation, Python-based data processing, and cloud data platforms.
You will work closely with data, biomedical, IT, and engineering teams to ensure data is reliable, scalable, and ready to support advanced analytics, predictive algorithms, and AI-driven solutions. The focus of this role is data engineering and analysis.
We offer theopportunity to work on data and AI initiatives that make a meaningful impact in healthcare technology.Also, a collaborative environment spanning data, biomedical, IT, and engineering teams.
Location:
Cambridge (Massachusetts, US) Work Mode:
on-site Type of contract:
C2C
Key Responsibilities: Retrieve data from AWS S3 and query and analyze datasets using Amazon Athena, SageMaker, and Jupyter Notebooks. Clean, transform, and prepare large datasets from multiple sources. Identify missing data, inconsistencies, time-alignment discrepancies, and artifact-prone measurements.
Data Management Develop and maintain data processing pipelines that transform and parse CSV, XML, and other flat-file formats into scalable databases and data structures such as InfluxDB. Develop and run Python and Bash scripts to automate ingestion, inventory, validation, and quality assessment of incoming device data.
Data Integrity Evaluation Investigate data gaps, anomalies, and integrity issues using Athena, SageMaker, or Jupyter Notebooks. Determine whether root causes relate to data curation, technical configuration, system performance, or storage limitations.
Troubleshooting and Collaboration Collaborate with biomedical and IT teams at partner sites to troubleshoot data transfer and collection issues and ensure reliable data availability. Work closely with internal engineering, data, and technical teams to investigate and resolve data-related issues.
Data Modeling and AI/ML Analyze and model physiological, vital-sign, and EMR data from multiple sources to support data-driven and AI/ML solutions. Develop, enhance, and evaluate predictive algorithms and machine learning models using multivariate data, including numerical and textual data.
Statistical Validation Cross-validate Python-based machine learning and regression results against outputs generated in SAS, in collaboration with biostatistics teams.
Data Simulation Prepare and transform retrospective CSV/XML datasets to generate simulated data using a Virtual Hospital Simulator. Support algorithm testing and validation within surveillance software. Contribute to improving data quality, scalability, reliability, and analytical capabilities across data and AI initiatives.
Required Qualifications: 7 to 10 years of professional experience in data engineering and data analysis . Hands-on AI/ML experience , including developing, evaluating, and enhancing predictive algorithms and machine learning and regression models using multivariate data (numerical and text-based). Experience with healthcare data , including physiological signals, vital signs, and EMR data. Background in the medical device industry , including experience working with device-generated clinical data. Strong proficiency in
Python
for data processing, analysis, and machine learning. Experience scripting in Bash for batch processing and automation. Hands-on experience with
AWS data services , including S3, Athena, and SageMaker. Experience working in
Jupyter Notebooks
for exploratory analysis and data investigation. Proven experience parsing and transforming flat-file formats (CSV, XML) into scalable databases, with exposure to time-series databases such as InfluxDB. Demonstrated ability to identify and resolve data quality issues, including missing data, time-alignment problems, and measurement artifacts. Strong troubleshooting skills and the ability to diagnose root causes across data, configuration, and infrastructure. Excellent communication skills, with the ability to collaborate effectively with technical and non-technical stakeholders across multiple organizations.
Why choose us An international community bringing together more than 110 different nationalities An environment where trust is central: 70% of our leaders started their careers at the entry level A strong training system with our internal Academy and more than 250 modules available A dynamic work environment that frequently comes together for internal events (afterworks, team buildings, etc.)
Amaris Consulting promotes equal opportunities. We are committed to bringing together people from diverse backgrounds and creating an inclusive work environment. In this regard, we welcome applications from all qualified individuals, regardless of sex, sexual orientation, race, ethnicity, beliefs, age, marital status, disability, or other characteristics.
#LI-AS2
Job description We are looking for a
Data & AI Engineer
to join our team and
contribute to data engineering, analysis, and AI/ML initiatives within a healthcare technology environment . The ideal candidate has strong experience working with large and complex datasets, data quality and transformation, Python-based data processing, and cloud data platforms.
You will work closely with data, biomedical, IT, and engineering teams to ensure data is reliable, scalable, and ready to support advanced analytics, predictive algorithms, and AI-driven solutions. The focus of this role is data engineering and analysis.
We offer theopportunity to work on data and AI initiatives that make a meaningful impact in healthcare technology.Also, a collaborative environment spanning data, biomedical, IT, and engineering teams.
Location:
Cambridge (Massachusetts, US) Work Mode:
on-site Type of contract:
C2C
Key Responsibilities: Data Wrangling Retrieve data from AWS S3 and query and analyze datasets using Amazon Athena, SageMaker, and Jupyter Notebooks. Clean, transform, and prepare large datasets from multiple sources. Identify missing data, inconsistencies, time-alignment discrepancies, and artifact-prone measurements.
Data Management Develop and maintain data processing pipelines that transform and parse CSV, XML, and other flat-file formats into scalable databases and data structures such as InfluxDB. Develop and run Python and Bash scripts to automate ingestion, inventory, validation, and quality assessment of incoming device data.
Data Integrity Evaluation Investigate data gaps, anomalies, and integrity issues using Athena, SageMaker, or Jupyter Notebooks. Determine whether root causes relate to data curation, technical configuration, system performance, or storage limitations.
Troubleshooting and Collaboration Collaborate with biomedical and IT teams at partner sites to troubleshoot data transfer and collection issues and ensure reliable data availability. Work closely with internal engineering, data, and technical teams to investigate and resolve data-related issues.
Data Modeling and AI/ML Analyze and model physiological, vital-sign, and EMR data from multiple sources to support data-driven and AI/ML solutions. Develop, enhance, and evaluate predictive algorithms and machine learning models using multivariate data, including numerical and textual data.
Statistical Validation Cross-validate Python-based machine learning and regression results against outputs generated in SAS, in collaboration with biostatistics teams.
Data Simulation Prepare and transform retrospective CSV/XML datasets to generate simulated data using a Virtual Hospital Simulator. Support algorithm testing and validation within surveillance software.
Continuous Improvement Contribute to improving data quality, scalability, reliability, and analytical capabilities across data and AI initiatives.
Required Qualifications: 7 to 10 years of professional experience in data engineering and data analysis . Hands-on AI/ML experience , including developing, evaluating, and enhancing predictive algorithms and machine learning and regression models using multivariate data (numerical and text-based). Experience with healthcare data , including physiological signals, vital signs, and EMR data. Background in the medical device industry , including experience working with device-generated clinical data. Strong proficiency in
Python
for data processing, analysis, and machine learning. Experience scripting in Bash for batch processing and automation.
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