Mission Lane Remote (USA) 1d ago $120,000 - $135,000
The posting lists Remote, United States. Collaborate on designing, developing, and deploying machine learning models to solve practical problems. Partner with business leaders and technical experts to develop new data sources. On the team you would Improve modeling methodology. On the team you would Apply models with sound risk management.
Key facts
Location: Remote (USA)
Compensation: Annual full-time starting base salary range: $120,000 - $135,000; Eligible for annual incentive and equity programs; Pay based on experience, education, certification(s), training, skills, and competencies; Comprehensive benefits: paid time off, 401(k) match, wellness stipend, health/dental/vision insurance, disability coverage, paid parental leave, flexible spending account, life insurance, remote-first environment
What you'll do
- Collaborate on designing, developing, and deploying machine learning models to solve practical problems.
- Partner with business leaders and technical experts to develop new data sources.
- You will Improve modeling methodology.
- You will Apply models with sound risk management.
- You will Share best practices for software engineering.
- Collaborate with experienced data scientists on operationalizing and evaluating models for real-world applications.
- Work as a generalist data scientist motivated by practical solutions.
- Practice software engineering fundamentals including test-driven development, code review, and refactoring.
- You will Use the PyData stack (numpy, scikit-learn, pandas, etc.).
- Engage with a wide range of ML solutions including Spark, Kubernetes, Airflow, MLFlow, Chalk, BentoML, DVC, and in-house tools.
Requirements
- Collaborated on creating, deploying, and managing supervised learning models in production systems.
- PhD in a quantitative field and 1+ years of experience in a related role.
- BS / MS in a quantitative field and 3+ years of experience in a related role.
- You need Share best practices and collaborate on complex technical problems.
- You need Generalist data scientist oriented toward practical solutions.
- Solid fundamentals in software engineering (test-driven development, code review, refactoring).
- You need Proficient with PyData stack (numpy, scikit-learn, pandas, etc.).
- Experience with Spark, Kubernetes, Airflow, MLFlow, Chalk, BentoML, DVC, and in-house tools.
The work touches V-BAT. The work touches Hivemind.
Practical notes
Note: Annual full-time starting base salary range: $120,000 - $135,000. Note: Eligible for annual incentive and equity programs. Pay based on experience, education, certification(s), training, skills, and competencies. Comprehensive benefits: paid time off, 401(k) match, wellness stipend, health/dental/vision insurance, disability coverage, paid parental leave, flexible spending account, life insurance, remote-first environment. Note: Collaborate on designing, developing, and deploying machine learning models to solve practical problems.
Partner with business leaders and technical experts to develop new data sources.
For , Improve modeling methodology.
For , Apply models with sound risk management.
For , Share best practices for software engineering.
Collaborate with experienced data scientists on operationalizing and evaluating models for real-world applications.
Work as a generalist data scientist motivated by practical solutions.
Practice software engineering fundamentals including test-driven development, code review, and refactoring.
For , Use the PyData stack (numpy, scikit-learn, pandas, etc.).
Engage with a wide range of ML solutions including Spark, Kubernetes, Airflow, MLFlow, Chalk, BentoML, DVC, and in-house tools.
Collaborated on creating, deploying, and managing supervised learning models in production systems.
PhD in a quantitative field and 1+ years of experience in a related role.
BS / MS in a quantitative field and 3+ years of experience in a related role.
For , Share best practices and collaborate on complex technical problems.
#J-18808-Ljbffr