Founding Data Scientist (>4yrs exp)

We are hiring our first Data Scientist to build the ML foundation for a high-velocity US focused startup

NOTE:

first Data Scientist

About Us

Inveno

What Problem Are We Solving and Why?

75% of US warehouses rely on

custom AI and optimization models

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.