Applied Scientist
KoBold MetalsSalary not specified
Full Time
Senior (5 to 8 years), Expert & Leadership (9+ years)
Not SpecifiedCompensation
Junior (1 to 2 years)Experience Level
Full TimeJob Type
UnknownVisa
Software, Data TechnologyIndustries
Requirements
- 5+ years of experience building and deploying ML models in production environments
- Strong foundation in machine learning, statistics, and deep learning fundamentals
- Expertise in Python and modern ML frameworks (PyTorch, TensorFlow, or similar)
- Experience with large-scale data processing and ML model optimization
- Experience with MLOps practices and tools (model versioning, monitoring, and deployment)
- Strong software engineering practices and experience with production systems
- Expert-level SQL skills with experience writing complex queries and optimizing query performance
- Ability to navigate (and bring structure to) ambiguity; ability to bring a project from 0 to 1, or scale a project from 1 to 100
Responsibilities
- Architect and implement scalable ML systems for modeling compensation within a single company and across the market as a whole
- Collaborate with product and engineering teams to identify additional opportunities to leverage ML-driven solutions
- Help evolve the technical direction of ML initiatives across the company
- Drive millions of dollars of revenue growth
Skills
Machine Learning
Data Science
Software Engineering
ML Systems
Data Modeling
Collaboration
Pave
Compensation management solutions for businesses
About Pave
Pave provides compensation management solutions designed for businesses of all sizes, particularly those backed by venture capital. Its main product is a suite of tools that integrate with existing HR Information Systems (HRIS), Applicant Tracking Systems (ATS), and Cap Table software, allowing companies to access real-time data for employee compensation planning and benchmarking. This integration helps eliminate the need for spreadsheets and manual data entry, enabling HR leaders to make informed decisions based on accurate information. Pave operates on a subscription-based model, offering various tiers that may include advanced analytics and personalized support. The company aims to simplify the compensation analysis process, making it easier for HR departments to ensure fair and competitive pay, which can lead to improved employee satisfaction and retention.
New York City, New YorkHeadquarters
2019Year Founded
$165.4MTotal Funding
SERIES_CCompany Stage
Data & Analytics, Enterprise SoftwareIndustries
1-10Employees
Benefits
Competitive salary
Equity
Medical, dental & vision coverage
Commuter benefits
Catered lunch
Unlimited PTO policy
Risks
Competition from established HR tech companies like Workday and ADP is intensifying.
Data privacy concerns may arise from integrating with multiple HR systems.
Economic downturns could impact Pave's growth due to reliance on VC-backed clients.
Differentiation
Pave offers real-time compensation data, eliminating the need for spreadsheets.
Seamless integration with HRIS, ATS, and Cap Table systems sets Pave apart.
Pave's platform is powered by the largest real-time compensation dataset globally.
Upsides
Partnership with UKG enhances Pave's platform with valuable organizational data.
Pave's subscription model aligns with growing trends in enterprise software.
Increased focus on pay equity boosts demand for Pave's data-driven solutions.
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