TurbineOne
Senior Data Scientist - Machine Learning Data Operations
TurbineOne, San Francisco, California, United States, 94199
Senior Data Scientist - Machine Learning Data Operations
ABOUT THE JOB
Company : TurbineOne — TurbineOne is the frontline perception company. We deliver decision advantage, better situational awareness, and stronger force protection. The company is a small, fast-moving startup backed by defense tech venture capitalists. TurbineOne is deployed by every branch of the Department of Defense to solve critical missions.
Reporting to the Machine Learning team lead
Geographically flexible for home-office
Responsibilities
Ingesting, organizing, and maintaining large-scale training datasets from open-source resources and contract-specific artifacts
Creating and managing data cataloging systems to ensure datasets are findable, accessible, and ready for ML training pipelines
Designing and implementing data labeling workflows, including managing external labeling vendors and quality assurance processes
Building and maintaining YOLO-style manifests and annotation formats for custom computer vision datasets
Performing data cleaning, validation, and augmentation to ensure high-quality training data
Conducting exploratory data analysis and generating insights about dataset characteristics, biases, and coverage gaps
Supporting the ML research team with statistical analysis, experiment design, and model evaluation
Developing data pipelines and automation tools for continuous data ingestion and processing
Collaborating with ML engineers to optimize data loading and preprocessing for training efficiency
On a Typical Day You Would
Process incoming datasets from various sources, performing quality checks and organizing them into our data management system
Create or review annotation schemas and coordinate with labeling teams to ensure consistent, high-quality labels
Write Python scripts to clean, transform, and validate datasets for specific ML training requirements
Analyze dataset statistics and create visualizations to identify potential issues or opportunities for improvement
Collaborate with the ML research lead to design experiments and evaluate model performance across different data splits
Document dataset characteristics, versioning, and lineage to maintain reproducibility and compliance
Desired Experience
High standard of ethics, grit, integrity and moral character
5+ years of experience in data science, analytics, or related field with focus on ML data preparation
Strong foundation in probability, statistics, and experimental design
Bachelor's degree in Statistics, Mathematics, Computer Science, or related quantitative field (Master's preferred)
Proficiency with Python data stack: Pandas, NumPy, Jupyter Notebooks, and data visualization libraries
Experience with ML frameworks (PyTorch, Scikit-learn) and familiarity with training workflows
Hands-on experience with computer vision datasets and annotation formats (COCO, YOLO, Pascal VOC)
Experience managing data labeling projects and working with annotation tools (Label Studio, CVAT, or similar)
Familiarity with open-source ML models and experience applying them to real-world problems
Strong SQL skills and experience with data warehousing concepts
Experience with version control (Git) and collaborative development practices
Excellent communication skills for coordinating with technical and non-technical stakeholders
Meticulous attention to detail and strong organizational skills for managing complex datasets
Willingness to embrace the Startup Culture of moving fast, being insatiably curious, celebrating often, embracing uncertainty, and having a personal desire to improve other peoples' lives
Nice to Have
Experience with defense or security-related datasets
Knowledge of edge computing constraints and data optimization techniques
Experience with distributed data processing frameworks (Spark, Dask)
Familiarity with MLOps practices and tools
Background in specific domains relevant to perception systems (satellite imagery, sensor fusion, etc.)
Startup Culture Expectations
We\'re a small, fully remote team and everything is our responsibility
Our team thrives on autonomy, trust and solid communication
Everyone on the Team needs to be very comfortable with constant change, moving fast, sharing failures, embracing grit, and building things themselves
Eligibility
Must be eligible to obtain a clearance with the U.S. government
Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Engineering and Information Technology
Industries
Defense & Space
#J-18808-Ljbffr
Company : TurbineOne — TurbineOne is the frontline perception company. We deliver decision advantage, better situational awareness, and stronger force protection. The company is a small, fast-moving startup backed by defense tech venture capitalists. TurbineOne is deployed by every branch of the Department of Defense to solve critical missions.
Reporting to the Machine Learning team lead
Geographically flexible for home-office
Responsibilities
Ingesting, organizing, and maintaining large-scale training datasets from open-source resources and contract-specific artifacts
Creating and managing data cataloging systems to ensure datasets are findable, accessible, and ready for ML training pipelines
Designing and implementing data labeling workflows, including managing external labeling vendors and quality assurance processes
Building and maintaining YOLO-style manifests and annotation formats for custom computer vision datasets
Performing data cleaning, validation, and augmentation to ensure high-quality training data
Conducting exploratory data analysis and generating insights about dataset characteristics, biases, and coverage gaps
Supporting the ML research team with statistical analysis, experiment design, and model evaluation
Developing data pipelines and automation tools for continuous data ingestion and processing
Collaborating with ML engineers to optimize data loading and preprocessing for training efficiency
On a Typical Day You Would
Process incoming datasets from various sources, performing quality checks and organizing them into our data management system
Create or review annotation schemas and coordinate with labeling teams to ensure consistent, high-quality labels
Write Python scripts to clean, transform, and validate datasets for specific ML training requirements
Analyze dataset statistics and create visualizations to identify potential issues or opportunities for improvement
Collaborate with the ML research lead to design experiments and evaluate model performance across different data splits
Document dataset characteristics, versioning, and lineage to maintain reproducibility and compliance
Desired Experience
High standard of ethics, grit, integrity and moral character
5+ years of experience in data science, analytics, or related field with focus on ML data preparation
Strong foundation in probability, statistics, and experimental design
Bachelor's degree in Statistics, Mathematics, Computer Science, or related quantitative field (Master's preferred)
Proficiency with Python data stack: Pandas, NumPy, Jupyter Notebooks, and data visualization libraries
Experience with ML frameworks (PyTorch, Scikit-learn) and familiarity with training workflows
Hands-on experience with computer vision datasets and annotation formats (COCO, YOLO, Pascal VOC)
Experience managing data labeling projects and working with annotation tools (Label Studio, CVAT, or similar)
Familiarity with open-source ML models and experience applying them to real-world problems
Strong SQL skills and experience with data warehousing concepts
Experience with version control (Git) and collaborative development practices
Excellent communication skills for coordinating with technical and non-technical stakeholders
Meticulous attention to detail and strong organizational skills for managing complex datasets
Willingness to embrace the Startup Culture of moving fast, being insatiably curious, celebrating often, embracing uncertainty, and having a personal desire to improve other peoples' lives
Nice to Have
Experience with defense or security-related datasets
Knowledge of edge computing constraints and data optimization techniques
Experience with distributed data processing frameworks (Spark, Dask)
Familiarity with MLOps practices and tools
Background in specific domains relevant to perception systems (satellite imagery, sensor fusion, etc.)
Startup Culture Expectations
We\'re a small, fully remote team and everything is our responsibility
Our team thrives on autonomy, trust and solid communication
Everyone on the Team needs to be very comfortable with constant change, moving fast, sharing failures, embracing grit, and building things themselves
Eligibility
Must be eligible to obtain a clearance with the U.S. government
Seniority level
Mid-Senior level
Employment type
Full-time
Job function
Engineering and Information Technology
Industries
Defense & Space
#J-18808-Ljbffr