Part-time Data Scientist, US Startup (>4yrs exp)

We are hiring a part-time Data Scientist with >4yrs exp to build the ML foundation for a high-velocity US focused startup.

~3 months of work, with potential for full-time (12 hours per week)

NOTE:

About Us

Inveno is a NYC based startup automating the manual resource planning that happens daily at US Warehouses.

What Problem Are We Solving and Why?

Today, 75% of US warehouses rely on manual shift planning, determining where staff go, which trucks unload where, which items get processed when, all without automation. This results in over $1 million in annual inefficiencies per warehouse.

We are here to change that, by harnessing operational data and developing custom AI and optimization models, we help warehouse managers plan with precision, speed, and scale.

Who’s Building This?

We’re a founding team of 3:

• A warehouse operations expert with 8 years of experience at top logistics startups in the US and India.
• A CEO who’s built and exited 3 companies and led teams at Alibaba Group.
• A CTO who scaled engineering and systems across multiple ventures internationally.

Our Progress So Far

• Our V1 product will be live with our first client in 2mths.
• We’ve secured a paid contract with a global multi-brand conglomerate, and have a promising pipeline with some of the largest retailers in the US.

Why Us?

• For a data scientist a few years into their career, it's an opportunity to step-up and own the development and deployment of a foundational model that powers a critical sector.

Key Responsibilities

• Lead Optimizer Development: Architect and implement our core optimization engine using tools like Google’s Math-OPT, NVIDIA cuOpt, or OR-Tools.
• Build Machine Learning Models: Create and deploy supervised learning models to support and enhance the optimizer using complex real-world warehouse data.
• Shape LLM Interaction Layer: Collaborate with engineering to design workflows where LLMs convert user input into operational rules that feed into the optimizer.
• Establish MLOps Best Practices: Set up model versioning, deployment, monitoring, and experimentation pipelines that ensure robust and scalable ML infrastructure.

Qualifications & Skills (Must-haves)

• Experience designing and deploying constraint-based optimization models using tools like Google OR-Tools, Gurobi, CPLEX, or similar.
• Advanced Python skills with fluency in the data science stack: NumPy, pandas, scikit-learn, etc.
• Strong foundation in supervised ML, including feature engineering, model selection, and validation techniques.
• Proven ability to translate complex operational problems into solvable mathematical or ML models.
• Bachelor’s or Master’s degree in Computer Science, Operations Research, Statistics, or a related quantitative field.