STEM Solutions
Data Scientist
About Us Our partner is a fast-growing startup built on a decade of Caltech research in partnership with NASA/JPL. Our mission is to transform mission readiness and operational efficiency at speed and scale by reshaping how maintenance and sustainment are approached. Our AI delivers essential insights to close critical capability gaps in maintenance, materiel, critical insights, and cyber.
The Role We are seeking a Machine Learning Engineer with hands-on experience deploying ML models in production environments, particularly in security-sensitive or classified settings. The ideal candidate will bring strong Python-based data engineering skills, expertise with modern ML frameworks, and the ability to lead end-to-end development in mission-critical environments.
Key Responsibilities Design, build, and deploy ML models into production systems, with a focus on security-sensitive or classified (SCIF) environments. Develop robust, production-ready Python code aligned with software engineering best practices. Leverage the Python data ecosystem (pandas, numpy, sklearn, tensorflow, pytorch, matplotlib) for data processing, model development, and analytics. Integrate applications with big data and streaming platforms (e.g., Spark, Dask, Snowpark, Kafka) and manage workflow orchestration (Airflow, Celery, Prefect). Apply containerization (Docker/Kubernetes), version control (Git), and database systems (SQL, NoSQL, Elasticsearch) to ensure scalability and reliability. Conduct cyber-focused analytics including PCAP analysis, network monitoring, CVE research, and vulnerability assessments. Collaborate across teams to ensure solutions address mission readiness needs in maintenance, materiel, critical insights, and cyber. Required Qualifications:
4+ years of professional Python-based data engineering experience with proven ML production deployments in secure environments, OR a degree from a top U.S. CS program (MIT, Stanford, CMU, Princeton, etc.) with demonstrated data science experience. At least 1 year of hands-on experience deploying ML models in production. Experience leading development efforts for a project from a SCIF. Bachelor's degree in Computer Science or related field from a top 100 U.S. university (per U.S. News rankings). 3+ years of experience writing production-ready Python code. 3+ years of experience in the Python data ecosystem (pandas, numpy, sklearn, tensorflow, pytorch, matplotlib). Strong experience with containerization (Docker/Kubernetes), version control (Git), and database systems (SQL/NoSQL/Elasticsearch). Demonstrated cybersecurity analytics experience, including network and vulnerability analysis. 1+ years integrating applications with big data/streaming technologies and workflow orchestration. Preferred Qualifications (Nice to Have)
Candidates who do not meet the required qualifications are often not advanced in our process. Prior experience supporting defense, aerospace, or government-focused readiness missions. Experience with mission data at enterprise scale. Soft Skills
Exceptional problem-solving skills and a sense of ownership in delivering mission-critical solutions. Strong written and verbal communication skills in English. Ability to operate effectively in fast-moving, high-security environments.
About Us Our partner is a fast-growing startup built on a decade of Caltech research in partnership with NASA/JPL. Our mission is to transform mission readiness and operational efficiency at speed and scale by reshaping how maintenance and sustainment are approached. Our AI delivers essential insights to close critical capability gaps in maintenance, materiel, critical insights, and cyber.
The Role We are seeking a Machine Learning Engineer with hands-on experience deploying ML models in production environments, particularly in security-sensitive or classified settings. The ideal candidate will bring strong Python-based data engineering skills, expertise with modern ML frameworks, and the ability to lead end-to-end development in mission-critical environments.
Key Responsibilities Design, build, and deploy ML models into production systems, with a focus on security-sensitive or classified (SCIF) environments. Develop robust, production-ready Python code aligned with software engineering best practices. Leverage the Python data ecosystem (pandas, numpy, sklearn, tensorflow, pytorch, matplotlib) for data processing, model development, and analytics. Integrate applications with big data and streaming platforms (e.g., Spark, Dask, Snowpark, Kafka) and manage workflow orchestration (Airflow, Celery, Prefect). Apply containerization (Docker/Kubernetes), version control (Git), and database systems (SQL, NoSQL, Elasticsearch) to ensure scalability and reliability. Conduct cyber-focused analytics including PCAP analysis, network monitoring, CVE research, and vulnerability assessments. Collaborate across teams to ensure solutions address mission readiness needs in maintenance, materiel, critical insights, and cyber. Required Qualifications:
4+ years of professional Python-based data engineering experience with proven ML production deployments in secure environments, OR a degree from a top U.S. CS program (MIT, Stanford, CMU, Princeton, etc.) with demonstrated data science experience. At least 1 year of hands-on experience deploying ML models in production. Experience leading development efforts for a project from a SCIF. Bachelor's degree in Computer Science or related field from a top 100 U.S. university (per U.S. News rankings). 3+ years of experience writing production-ready Python code. 3+ years of experience in the Python data ecosystem (pandas, numpy, sklearn, tensorflow, pytorch, matplotlib). Strong experience with containerization (Docker/Kubernetes), version control (Git), and database systems (SQL/NoSQL/Elasticsearch). Demonstrated cybersecurity analytics experience, including network and vulnerability analysis. 1+ years integrating applications with big data/streaming technologies and workflow orchestration. Preferred Qualifications (Nice to Have)
Candidates who do not meet the required qualifications are often not advanced in our process. Prior experience supporting defense, aerospace, or government-focused readiness missions. Experience with mission data at enterprise scale. Soft Skills
Exceptional problem-solving skills and a sense of ownership in delivering mission-critical solutions. Strong written and verbal communication skills in English. Ability to operate effectively in fast-moving, high-security environments.