We are hiring a Data Scientist (part-time) with a few years of experience. Expectation: about 15-20 hours per week for 3mths. Can turn into full-time for a strong performer. Join us and help us build the models that will define how resources are planned across Warehouses all over the US. You’ll lay the ML foundation to scale across thousands of warehouses and millions of packages. About Us Inveno is a US-based startup revolutionizing warehouse operations with intelligent resource planning powered by specialized AI. We’re on a mission to eliminate manual inefficiencies in warehouse management by building custom models that deliver smart, data-driven planning. 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 at Bangladesh’s top-funded startup. Our Progress So Far Our V1 product is currently in development. We’ve already secured a paid contract with the global brand house with other major brands in the pipeline (including top US retailers) Key Responsibilities Lead Optimizer Development: Architect and implement our core optimization engine using tools like Google’s Math-OPT, NVIDIA cuOpt, or OR-Tools. Focus on improving labor planning, dock assignments, and wave planning to ensure on-time fulfillment. 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.
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: Data Analytics is not Data Science. 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.
We are hiring our first Data Scientist to build the ML foundation for a high-velocity US focused startup NOTE: Data Analytics is not Data Science. Join our tech team as the first Data Scientist and build the models that will define how resources are planned across Warehouses all over the US. You’ll lay the ML foundation to scale across thousands of warehouses and millions of packages. 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.
As a part-time Data Scientist with more than 4 years of experience, you will have the opportunity to build the machine learning foundation for a high-velocity US-focused startup. The position involves approximately 3 months of work initially, with the potential for full-time engagement (12 hours per week). Inveno, a New York City-based startup, is dedicated to automating the manual resource planning processes that occur daily in US warehouses. The primary problem being addressed is the prevalent manual shift planning in 75% of US warehouses, leading to inefficiencies costing over $1 million annually per warehouse. By leveraging operational data and developing custom AI and optimization models, Inveno aims to empower warehouse managers with precise, efficient, and scalable planning solutions. The founding team of Inveno consists of three members: - A warehouse operations expert with 8 years of experience at prominent logistics startups in the US and India. - A CEO with a track record of building and exiting three companies, as well as leading teams at Alibaba Group. - A CTO experienced in scaling engineering and systems across various international ventures. In terms of progress, Inveno's V1 product is scheduled to go live with the first client within 2 months. The startup has already secured a paid contract with a global multi-brand conglomerate and has a promising pipeline including some of the largest retailers in the US. As a data scientist joining the team, you will play a crucial role in leading the development of the core optimization engine, building machine learning models to support the optimizer, shaping the LLM interaction layer, and establishing MLOps best practices. Key responsibilities include architecting and implementing the core optimization engine, creating and deploying supervised learning models, collaborating with engineering on workflow design, and setting up model versioning, deployment, monitoring, and experimentation pipelines. Qualifications & Skills Required: - Experience in designing and deploying constraint-based optimization models using tools like Google OR-Tools, Gurobi, CPLEX, or similar. - Proficiency in advanced Python skills and familiarity with the data science stack including NumPy, pandas, and scikit-learn. - Strong foundation in supervised machine learning, encompassing feature engineering, model selection, and validation techniques. - Demonstrated ability to translate complex operational problems into solvable mathematical or machine learning models. - Bachelor's or Master's degree in Computer Science, Operations Research, Statistics, or a related quantitative field. Joining Inveno as a Data Scientist presents a significant opportunity for career advancement by taking ownership of the development and deployment of a foundational model that drives innovation in a critical sector.,
As a part-time Data Scientist with over 4 years of experience, you will be responsible for building the Machine Learning foundation for a high-velocity US-focused startup. The position offers approximately 3 months of work initially, with the potential for a full-time role requiring 12 hours per week. It is important to note that Data Analytics is distinguished from Data Science. Inveno, a New York City-based startup, aims to automate the manual resource planning processes that occur daily at warehouses across the United States. The current problem being addressed is the reliance of 75% of US warehouses on manual shift planning, leading to inefficiencies amounting to over $1 million per warehouse annually. By utilizing operational data and developing custom AI and optimization models, Inveno seeks to assist warehouse managers in planning with precision, speed, and scalability. The founding team behind Inveno consists of a warehouse operations expert with 8 years of experience, a CEO with a successful track record of building and exiting companies, and a CTO experienced in scaling engineering and systems across various ventures globally. The company has made significant progress, with the V1 product set to go live with the first client in 2 months. Inveno has also secured a paid contract with a global multi-brand conglomerate and has a promising pipeline with some of the largest retailers in the US. As a Data Scientist in this role, you will be responsible for leading the development of the optimizer, building machine learning models, collaborating on the LLM interaction layer, and establishing MLOps best practices. Key responsibilities include architecting and implementing core optimization engines, creating supervised learning models, designing workflows, and setting up model versioning and deployment pipelines. Qualifications and skills required for this position include experience in designing and deploying constraint-based optimization models, advanced Python skills, a strong foundation in supervised machine learning, the ability to translate complex operational problems into solvable models, and a degree in Computer Science, Operations Research, Statistics, or a related quantitative field.,